{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Supervised Sequence Classification"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "While unsupervised methods can be powerful to identify antigen-specific sequences, being able to leverage known labels to guide the learning process can provide for better results, provided there is a sufficient amount of data to learn from. The first type of supervised learning we will explore within DeepTCR is being able to correctly classify a given TCR sequence to some label (i.e. its antigen specificity) from using its sequence information."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First we will load data from the Murine dataset which has TCR sequences from 9 murine antigens with beta-chain information including sequence, v-beta, and j-beta gene usage."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loading Data...\n",
      "Embedding Sequences...\n",
      "Data Loaded\n"
     ]
    }
   ],
   "source": [
    "import sys\n",
    "sys.path.append('../../')\n",
    "from DeepTCR.DeepTCR import DeepTCR_SS\n",
    "\n",
    "# Instantiate training object\n",
    "DTCR_SS = DeepTCR_SS('Tutorial')\n",
    "\n",
    "#Load Data from directories\n",
    "DTCR_SS.Get_Data(directory='../../Data/Murine_Antigens',Load_Prev_Data=False,aggregate_by_aa=True,\n",
    "               aa_column_beta=0,count_column=1,v_beta_column=2,j_beta_column=3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will then train the sequence classifier as follows. First, we will split the dataset into a train,validation,and independent test cohort so we can assess how generalizable our model will be to new unseen data.And then we will train our sequene classifier. Our test_size parameter will tell DeepTCR how much of the data to leave out for valid/test which is split evenly across these two."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING: Logging before flag parsing goes to stderr.\n",
      "W1004 19:35:15.295836 140338515621696 deprecation_wrapper.py:119] From ../../DeepTCR/functions/Layers.py:130: The name tf.placeholder is deprecated. Please use tf.compat.v1.placeholder instead.\n",
      "\n",
      "W1004 19:35:15.304923 140338515621696 deprecation_wrapper.py:119] From ../../DeepTCR/functions/Layers.py:140: The name tf.placeholder_with_default is deprecated. Please use tf.compat.v1.placeholder_with_default instead.\n",
      "\n",
      "W1004 19:35:15.308014 140338515621696 deprecation_wrapper.py:119] From ../../DeepTCR/functions/Layers.py:141: The name tf.sparse.placeholder is deprecated. Please use tf.compat.v1.sparse.placeholder instead.\n",
      "\n",
      "W1004 19:35:15.317007 140338515621696 deprecation_wrapper.py:119] From ../../DeepTCR/functions/Layers.py:12: The name tf.get_variable is deprecated. Please use tf.compat.v1.get_variable instead.\n",
      "\n",
      "W1004 19:35:15.319694 140338515621696 deprecation.py:506] From /usr/local/lib/python3.6/dist-packages/tensorflow/python/ops/init_ops.py:1251: calling VarianceScaling.__init__ (from tensorflow.python.ops.init_ops) with dtype is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Call initializer instance with the dtype argument instead of passing it to the constructor\n",
      "W1004 19:35:15.349380 140338515621696 deprecation_wrapper.py:119] From ../../DeepTCR/functions/Layers.py:156: The name tf.variable_scope is deprecated. Please use tf.compat.v1.variable_scope instead.\n",
      "\n",
      "W1004 19:35:15.387006 140338515621696 deprecation.py:323] From ../../DeepTCR/functions/Layers.py:98: conv2d (from tensorflow.python.layers.convolutional) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Use `tf.keras.layers.Conv2D` instead.\n",
      "W1004 19:35:15.653667 140338515621696 deprecation.py:323] From ../../DeepTCR/functions/Layers.py:99: flatten (from tensorflow.python.layers.core) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Use keras.layers.flatten instead.\n",
      "W1004 19:35:15.902306 140338515621696 deprecation.py:323] From ../../DeepTCR/functions/Layers.py:102: dropout (from tensorflow.python.layers.core) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Use keras.layers.dropout instead.\n",
      "W1004 19:35:16.017454 140338515621696 deprecation.py:323] From ../../DeepTCR/DeepTCR.py:2704: dense (from tensorflow.python.layers.core) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Use keras.layers.dense instead.\n",
      "W1004 19:35:16.236135 140338515621696 deprecation_wrapper.py:119] From ../../DeepTCR/DeepTCR.py:2723: The name tf.train.AdamOptimizer is deprecated. Please use tf.compat.v1.train.AdamOptimizer instead.\n",
      "\n",
      "W1004 19:35:16.600517 140338515621696 deprecation_wrapper.py:119] From ../../DeepTCR/DeepTCR.py:2734: The name tf.train.Saver is deprecated. Please use tf.compat.v1.train.Saver instead.\n",
      "\n",
      "W1004 19:35:16.630304 140338515621696 deprecation_wrapper.py:119] From ../../DeepTCR/DeepTCR.py:2738: The name tf.reset_default_graph is deprecated. Please use tf.compat.v1.reset_default_graph instead.\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training_Statistics: \n",
      " Epoch: 1/10000 Training loss: 2.21900 Validation loss: 2.11338 Testing loss: 2.11895 Training Accuracy: 0.15009 Validation Accuracy: 0.19344 Testing AUC: 0.48558\n",
      "Training_Statistics: \n",
      " Epoch: 2/10000 Training loss: 2.08083 Validation loss: 1.99542 Testing loss: 1.99994 Training Accuracy: 0.26232 Validation Accuracy: 0.36393 Testing AUC: 0.52077\n",
      "Training_Statistics: \n",
      " Epoch: 3/10000 Training loss: 1.96063 Validation loss: 1.89899 Testing loss: 1.90433 Training Accuracy: 0.36298 Validation Accuracy: 0.3541 Testing AUC: 0.54812\n",
      "Training_Statistics: \n",
      " Epoch: 4/10000 Training loss: 1.87397 Validation loss: 1.85354 Testing loss: 1.86099 Training Accuracy: 0.35722 Validation Accuracy: 0.3541 Testing AUC: 0.57134\n",
      "Training_Statistics: \n",
      " Epoch: 5/10000 Training loss: 1.84259 Validation loss: 1.85090 Testing loss: 1.85929 Training Accuracy: 0.35375 Validation Accuracy: 0.3541 Testing AUC: 0.59919\n",
      "Training_Statistics: \n",
      " Epoch: 6/10000 Training loss: 1.83596 Validation loss: 1.82281 Testing loss: 1.82996 Training Accuracy: 0.35461 Validation Accuracy: 0.3541 Testing AUC: 0.63175\n",
      "Training_Statistics: \n",
      " Epoch: 7/10000 Training loss: 1.79496 Validation loss: 1.79094 Testing loss: 1.79566 Training Accuracy: 0.35751 Validation Accuracy: 0.38361 Testing AUC: 0.65791\n",
      "Training_Statistics: \n",
      " Epoch: 8/10000 Training loss: 1.76865 Validation loss: 1.77193 Testing loss: 1.77408 Training Accuracy: 0.39756 Validation Accuracy: 0.40328 Testing AUC: 0.67634\n",
      "Training_Statistics: \n",
      " Epoch: 9/10000 Training loss: 1.74662 Validation loss: 1.75604 Testing loss: 1.75581 Training Accuracy: 0.43103 Validation Accuracy: 0.41967 Testing AUC: 0.69424\n",
      "Training_Statistics: \n",
      " Epoch: 10/10000 Training loss: 1.72606 Validation loss: 1.73778 Testing loss: 1.73577 Training Accuracy: 0.4416 Validation Accuracy: 0.40984 Testing AUC: 0.7112\n",
      "Training_Statistics: \n",
      " Epoch: 11/10000 Training loss: 1.70485 Validation loss: 1.71603 Testing loss: 1.71247 Training Accuracy: 0.43843 Validation Accuracy: 0.41639 Testing AUC: 0.72559\n",
      "Training_Statistics: \n",
      " Epoch: 12/10000 Training loss: 1.67862 Validation loss: 1.69401 Testing loss: 1.68912 Training Accuracy: 0.43546 Validation Accuracy: 0.41639 Testing AUC: 0.73751\n",
      "Training_Statistics: \n",
      " Epoch: 13/10000 Training loss: 1.65384 Validation loss: 1.67319 Testing loss: 1.66665 Training Accuracy: 0.43382 Validation Accuracy: 0.41639 Testing AUC: 0.74782\n",
      "Training_Statistics: \n",
      " Epoch: 14/10000 Training loss: 1.62962 Validation loss: 1.65288 Testing loss: 1.64379 Training Accuracy: 0.43189 Validation Accuracy: 0.40984 Testing AUC: 0.75649\n",
      "Training_Statistics: \n",
      " Epoch: 15/10000 Training loss: 1.60415 Validation loss: 1.63272 Testing loss: 1.62071 Training Accuracy: 0.43589 Validation Accuracy: 0.41967 Testing AUC: 0.76392\n",
      "Training_Statistics: \n",
      " Epoch: 16/10000 Training loss: 1.57530 Validation loss: 1.61354 Testing loss: 1.59828 Training Accuracy: 0.44862 Validation Accuracy: 0.44262 Testing AUC: 0.77074\n",
      "Training_Statistics: \n",
      " Epoch: 17/10000 Training loss: 1.55587 Validation loss: 1.59476 Testing loss: 1.57602 Training Accuracy: 0.45078 Validation Accuracy: 0.44918 Testing AUC: 0.77699\n",
      "Training_Statistics: \n",
      " Epoch: 18/10000 Training loss: 1.53141 Validation loss: 1.57466 Testing loss: 1.55182 Training Accuracy: 0.45982 Validation Accuracy: 0.47541 Testing AUC: 0.78381\n",
      "Training_Statistics: \n",
      " Epoch: 19/10000 Training loss: 1.50255 Validation loss: 1.55504 Testing loss: 1.52753 Training Accuracy: 0.47058 Validation Accuracy: 0.48525 Testing AUC: 0.78956\n",
      "Training_Statistics: \n",
      " Epoch: 20/10000 Training loss: 1.47823 Validation loss: 1.53671 Testing loss: 1.50486 Training Accuracy: 0.49012 Validation Accuracy: 0.47541 Testing AUC: 0.7933\n",
      "Training_Statistics: \n",
      " Epoch: 21/10000 Training loss: 1.45202 Validation loss: 1.51972 Testing loss: 1.48244 Training Accuracy: 0.50059 Validation Accuracy: 0.48525 Testing AUC: 0.79647\n",
      "Training_Statistics: \n",
      " Epoch: 22/10000 Training loss: 1.43028 Validation loss: 1.50369 Testing loss: 1.46027 Training Accuracy: 0.51002 Validation Accuracy: 0.48525 Testing AUC: 0.79896\n",
      "Training_Statistics: \n",
      " Epoch: 23/10000 Training loss: 1.40398 Validation loss: 1.48914 Testing loss: 1.44014 Training Accuracy: 0.51506 Validation Accuracy: 0.48525 Testing AUC: 0.80117\n",
      "Training_Statistics: \n",
      " Epoch: 24/10000 Training loss: 1.37448 Validation loss: 1.47654 Testing loss: 1.42242 Training Accuracy: 0.52847 Validation Accuracy: 0.48525 Testing AUC: 0.80269\n",
      "Training_Statistics: \n",
      " Epoch: 25/10000 Training loss: 1.35701 Validation loss: 1.46313 Testing loss: 1.40485 Training Accuracy: 0.53914 Validation Accuracy: 0.47541 Testing AUC: 0.80482\n",
      "Training_Statistics: \n",
      " Epoch: 26/10000 Training loss: 1.33367 Validation loss: 1.45159 Testing loss: 1.38814 Training Accuracy: 0.54035 Validation Accuracy: 0.46885 Testing AUC: 0.80722\n",
      "Training_Statistics: \n",
      " Epoch: 27/10000 Training loss: 1.31132 Validation loss: 1.44218 Testing loss: 1.37145 Training Accuracy: 0.55428 Validation Accuracy: 0.49836 Testing AUC: 0.8095\n",
      "Training_Statistics: \n",
      " Epoch: 28/10000 Training loss: 1.29344 Validation loss: 1.43273 Testing loss: 1.35693 Training Accuracy: 0.56061 Validation Accuracy: 0.50164 Testing AUC: 0.81189\n",
      "Training_Statistics: \n",
      " Epoch: 29/10000 Training loss: 1.27033 Validation loss: 1.42535 Testing loss: 1.34476 Training Accuracy: 0.56411 Validation Accuracy: 0.49836 Testing AUC: 0.81375\n",
      "Training_Statistics: \n",
      " Epoch: 30/10000 Training loss: 1.25654 Validation loss: 1.41801 Testing loss: 1.33388 Training Accuracy: 0.56875 Validation Accuracy: 0.49508 Testing AUC: 0.81531\n",
      "Training_Statistics: \n",
      " Epoch: 31/10000 Training loss: 1.23363 Validation loss: 1.41186 Testing loss: 1.32454 Training Accuracy: 0.58341 Validation Accuracy: 0.4918 Testing AUC: 0.81743\n",
      "Training_Statistics: \n",
      " Epoch: 32/10000 Training loss: 1.22002 Validation loss: 1.40695 Testing loss: 1.31545 Training Accuracy: 0.59305 Validation Accuracy: 0.4918 Testing AUC: 0.81887\n",
      "Training_Statistics: \n",
      " Epoch: 33/10000 Training loss: 1.20664 Validation loss: 1.40042 Testing loss: 1.30571 Training Accuracy: 0.60026 Validation Accuracy: 0.50492 Testing AUC: 0.82046\n",
      "Training_Statistics: \n",
      " Epoch: 34/10000 Training loss: 1.18188 Validation loss: 1.39488 Testing loss: 1.29721 Training Accuracy: 0.61184 Validation Accuracy: 0.51475 Testing AUC: 0.82333\n",
      "Training_Statistics: \n",
      " Epoch: 35/10000 Training loss: 1.16577 Validation loss: 1.38848 Testing loss: 1.29007 Training Accuracy: 0.61663 Validation Accuracy: 0.48852 Testing AUC: 0.82516\n",
      "Training_Statistics: \n",
      " Epoch: 36/10000 Training loss: 1.14923 Validation loss: 1.38555 Testing loss: 1.28601 Training Accuracy: 0.61909 Validation Accuracy: 0.51803 Testing AUC: 0.82622\n",
      "Training_Statistics: \n",
      " Epoch: 37/10000 Training loss: 1.13607 Validation loss: 1.38106 Testing loss: 1.27983 Training Accuracy: 0.6277 Validation Accuracy: 0.52459 Testing AUC: 0.82742\n",
      "Training_Statistics: \n",
      " Epoch: 38/10000 Training loss: 1.12000 Validation loss: 1.37252 Testing loss: 1.27077 Training Accuracy: 0.63432 Validation Accuracy: 0.51475 Testing AUC: 0.83063\n",
      "Training_Statistics: \n",
      " Epoch: 39/10000 Training loss: 1.10512 Validation loss: 1.36568 Testing loss: 1.26585 Training Accuracy: 0.63649 Validation Accuracy: 0.53115 Testing AUC: 0.83135\n",
      "Training_Statistics: \n",
      " Epoch: 40/10000 Training loss: 1.09831 Validation loss: 1.36080 Testing loss: 1.25941 Training Accuracy: 0.64546 Validation Accuracy: 0.53443 Testing AUC: 0.83289\n",
      "Training_Statistics: \n",
      " Epoch: 41/10000 Training loss: 1.08142 Validation loss: 1.35780 Testing loss: 1.25653 Training Accuracy: 0.64496 Validation Accuracy: 0.51803 Testing AUC: 0.83439\n",
      "Training_Statistics: \n",
      " Epoch: 42/10000 Training loss: 1.06662 Validation loss: 1.35036 Testing loss: 1.25051 Training Accuracy: 0.64567 Validation Accuracy: 0.5377 Testing AUC: 0.8352\n",
      "Training_Statistics: \n",
      " Epoch: 43/10000 Training loss: 1.05558 Validation loss: 1.34349 Testing loss: 1.24465 Training Accuracy: 0.65603 Validation Accuracy: 0.5377 Testing AUC: 0.83698\n",
      "Training_Statistics: \n",
      " Epoch: 44/10000 Training loss: 1.04409 Validation loss: 1.33955 Testing loss: 1.24265 Training Accuracy: 0.66246 Validation Accuracy: 0.53443 Testing AUC: 0.8378\n",
      "Training_Statistics: \n",
      " Epoch: 45/10000 Training loss: 1.02460 Validation loss: 1.33961 Testing loss: 1.24255 Training Accuracy: 0.66354 Validation Accuracy: 0.54426 Testing AUC: 0.83779\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training_Statistics: \n",
      " Epoch: 46/10000 Training loss: 1.01209 Validation loss: 1.33730 Testing loss: 1.24025 Training Accuracy: 0.66878 Validation Accuracy: 0.52787 Testing AUC: 0.83979\n",
      "Training_Statistics: \n",
      " Epoch: 47/10000 Training loss: 1.00376 Validation loss: 1.33297 Testing loss: 1.23769 Training Accuracy: 0.66832 Validation Accuracy: 0.53443 Testing AUC: 0.84041\n",
      "Training_Statistics: \n",
      " Epoch: 48/10000 Training loss: 0.98702 Validation loss: 1.33136 Testing loss: 1.23639 Training Accuracy: 0.68198 Validation Accuracy: 0.54426 Testing AUC: 0.84129\n",
      "Training_Statistics: \n",
      " Epoch: 49/10000 Training loss: 0.97562 Validation loss: 1.33256 Testing loss: 1.23684 Training Accuracy: 0.68079 Validation Accuracy: 0.53115 Testing AUC: 0.84243\n",
      "Training_Statistics: \n",
      " Epoch: 50/10000 Training loss: 0.96215 Validation loss: 1.32804 Testing loss: 1.23471 Training Accuracy: 0.68079 Validation Accuracy: 0.5377 Testing AUC: 0.84259\n",
      "Training_Statistics: \n",
      " Epoch: 51/10000 Training loss: 0.94967 Validation loss: 1.32485 Testing loss: 1.23134 Training Accuracy: 0.69541 Validation Accuracy: 0.53443 Testing AUC: 0.84362\n",
      "Training_Statistics: \n",
      " Epoch: 52/10000 Training loss: 0.93825 Validation loss: 1.32484 Testing loss: 1.23184 Training Accuracy: 0.69448 Validation Accuracy: 0.53443 Testing AUC: 0.84422\n",
      "Training_Statistics: \n",
      " Epoch: 53/10000 Training loss: 0.92696 Validation loss: 1.32465 Testing loss: 1.23303 Training Accuracy: 0.69208 Validation Accuracy: 0.53115 Testing AUC: 0.84359\n",
      "Training_Statistics: \n",
      " Epoch: 54/10000 Training loss: 0.92133 Validation loss: 1.32394 Testing loss: 1.23330 Training Accuracy: 0.6984 Validation Accuracy: 0.54098 Testing AUC: 0.84439\n",
      "Training_Statistics: \n",
      " Epoch: 55/10000 Training loss: 0.90305 Validation loss: 1.32414 Testing loss: 1.23529 Training Accuracy: 0.70508 Validation Accuracy: 0.56066 Testing AUC: 0.8449\n",
      "Training_Statistics: \n",
      " Epoch: 56/10000 Training loss: 0.89273 Validation loss: 1.31914 Testing loss: 1.23072 Training Accuracy: 0.70837 Validation Accuracy: 0.54098 Testing AUC: 0.84592\n",
      "Training_Statistics: \n",
      " Epoch: 57/10000 Training loss: 0.88469 Validation loss: 1.32317 Testing loss: 1.23508 Training Accuracy: 0.71144 Validation Accuracy: 0.54426 Testing AUC: 0.84497\n",
      "Training_Statistics: \n",
      " Epoch: 58/10000 Training loss: 0.87296 Validation loss: 1.32268 Testing loss: 1.23663 Training Accuracy: 0.7188 Validation Accuracy: 0.55082 Testing AUC: 0.8446\n",
      "Training_Statistics: \n",
      " Epoch: 59/10000 Training loss: 0.85936 Validation loss: 1.31934 Testing loss: 1.23333 Training Accuracy: 0.72145 Validation Accuracy: 0.54754 Testing AUC: 0.84519\n",
      "Training_Statistics: \n",
      " Epoch: 60/10000 Training loss: 0.84614 Validation loss: 1.32223 Testing loss: 1.23551 Training Accuracy: 0.73117 Validation Accuracy: 0.55738 Testing AUC: 0.84579\n",
      "Training_Statistics: \n",
      " Epoch: 61/10000 Training loss: 0.83497 Validation loss: 1.32336 Testing loss: 1.23707 Training Accuracy: 0.73302 Validation Accuracy: 0.56066 Testing AUC: 0.846\n",
      "Training_Statistics: \n",
      " Epoch: 62/10000 Training loss: 0.82390 Validation loss: 1.32363 Testing loss: 1.23754 Training Accuracy: 0.73574 Validation Accuracy: 0.54754 Testing AUC: 0.84487\n",
      "Training_Statistics: \n",
      " Epoch: 63/10000 Training loss: 0.81314 Validation loss: 1.32350 Testing loss: 1.23815 Training Accuracy: 0.74767 Validation Accuracy: 0.56721 Testing AUC: 0.84569\n",
      "Training_Statistics: \n",
      " Epoch: 64/10000 Training loss: 0.80214 Validation loss: 1.32829 Testing loss: 1.24184 Training Accuracy: 0.7506 Validation Accuracy: 0.5541 Testing AUC: 0.8443\n",
      "Done Training\n"
     ]
    }
   ],
   "source": [
    "DTCR_SS.Get_Train_Valid_Test(test_size=0.25)\n",
    "DTCR_SS.Train()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "When we are done training, we can assess how well our classifier performs on the independent test set via looking at the ROC curves."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "DTCR_SS.AUC_Curve()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can also train our classifier with two other methods that allow for multiple iterations including a Monte Carlo method and K-Fold Cross-Validation Method."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For the Monte-Carlo method, we will specify the number of times we want to train train the classifier and the test size we want for each iteration. Of note, all parameters available for the Train method are also inputs for the Monte-Carlo and K-Fold Cross-Validation method."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n",
      "Training_Statistics: \n",
      " Epoch: 1/10000 Training loss: 2.21722 Validation loss: 2.12657 Testing loss: 2.11480 Training Accuracy: 0.11205 Validation Accuracy: 0.2623 Testing AUC: 0.5008\n",
      "Training_Statistics: \n",
      " Epoch: 2/10000 Training loss: 2.08199 Validation loss: 2.00179 Testing loss: 1.99444 Training Accuracy: 0.32465 Validation Accuracy: 0.3541 Testing AUC: 0.53707\n",
      "Training_Statistics: \n",
      " Epoch: 3/10000 Training loss: 1.96362 Validation loss: 1.90090 Testing loss: 1.89859 Training Accuracy: 0.3539 Validation Accuracy: 0.3541 Testing AUC: 0.56552\n",
      "Training_Statistics: \n",
      " Epoch: 4/10000 Training loss: 1.88100 Validation loss: 1.86092 Testing loss: 1.86510 Training Accuracy: 0.35375 Validation Accuracy: 0.3541 Testing AUC: 0.58776\n",
      "Training_Statistics: \n",
      " Epoch: 5/10000 Training loss: 1.85087 Validation loss: 1.85116 Testing loss: 1.85905 Training Accuracy: 0.35558 Validation Accuracy: 0.3541 Testing AUC: 0.61446\n",
      "Training_Statistics: \n",
      " Epoch: 6/10000 Training loss: 1.83665 Validation loss: 1.82411 Testing loss: 1.83248 Training Accuracy: 0.35526 Validation Accuracy: 0.3541 Testing AUC: 0.64166\n",
      "Training_Statistics: \n",
      " Epoch: 7/10000 Training loss: 1.81200 Validation loss: 1.79717 Testing loss: 1.80602 Training Accuracy: 0.3584 Validation Accuracy: 0.41967 Testing AUC: 0.6638\n",
      "Training_Statistics: \n",
      " Epoch: 8/10000 Training loss: 1.78563 Validation loss: 1.77355 Testing loss: 1.78360 Training Accuracy: 0.40349 Validation Accuracy: 0.45246 Testing AUC: 0.68294\n",
      "Training_Statistics: \n",
      " Epoch: 9/10000 Training loss: 1.76357 Validation loss: 1.75141 Testing loss: 1.76417 Training Accuracy: 0.43057 Validation Accuracy: 0.44918 Testing AUC: 0.69693\n",
      "Training_Statistics: \n",
      " Epoch: 10/10000 Training loss: 1.74361 Validation loss: 1.72727 Testing loss: 1.74320 Training Accuracy: 0.42281 Validation Accuracy: 0.4459 Testing AUC: 0.7098\n",
      "Training_Statistics: \n",
      " Epoch: 11/10000 Training loss: 1.72037 Validation loss: 1.70195 Testing loss: 1.72079 Training Accuracy: 0.42367 Validation Accuracy: 0.45246 Testing AUC: 0.72084\n",
      "Training_Statistics: \n",
      " Epoch: 12/10000 Training loss: 1.69666 Validation loss: 1.67665 Testing loss: 1.69814 Training Accuracy: 0.4216 Validation Accuracy: 0.45902 Testing AUC: 0.73212\n",
      "Training_Statistics: \n",
      " Epoch: 13/10000 Training loss: 1.67206 Validation loss: 1.65150 Testing loss: 1.67495 Training Accuracy: 0.4285 Validation Accuracy: 0.44918 Testing AUC: 0.74226\n",
      "Training_Statistics: \n",
      " Epoch: 14/10000 Training loss: 1.64468 Validation loss: 1.62609 Testing loss: 1.65103 Training Accuracy: 0.43707 Validation Accuracy: 0.45246 Testing AUC: 0.75382\n",
      "Training_Statistics: \n",
      " Epoch: 15/10000 Training loss: 1.61839 Validation loss: 1.60046 Testing loss: 1.62714 Training Accuracy: 0.44172 Validation Accuracy: 0.44918 Testing AUC: 0.76451\n",
      "Training_Statistics: \n",
      " Epoch: 16/10000 Training loss: 1.59530 Validation loss: 1.57373 Testing loss: 1.60310 Training Accuracy: 0.4421 Validation Accuracy: 0.44262 Testing AUC: 0.77394\n",
      "Training_Statistics: \n",
      " Epoch: 17/10000 Training loss: 1.56850 Validation loss: 1.54512 Testing loss: 1.57786 Training Accuracy: 0.44674 Validation Accuracy: 0.45246 Testing AUC: 0.7827\n",
      "Training_Statistics: \n",
      " Epoch: 18/10000 Training loss: 1.54188 Validation loss: 1.51606 Testing loss: 1.55223 Training Accuracy: 0.454 Validation Accuracy: 0.46557 Testing AUC: 0.79054\n",
      "Training_Statistics: \n",
      " Epoch: 19/10000 Training loss: 1.50735 Validation loss: 1.48702 Testing loss: 1.52676 Training Accuracy: 0.4688 Validation Accuracy: 0.47869 Testing AUC: 0.7974\n",
      "Training_Statistics: \n",
      " Epoch: 20/10000 Training loss: 1.48195 Validation loss: 1.45981 Testing loss: 1.50212 Training Accuracy: 0.48537 Validation Accuracy: 0.5082 Testing AUC: 0.80465\n",
      "Training_Statistics: \n",
      " Epoch: 21/10000 Training loss: 1.45898 Validation loss: 1.43420 Testing loss: 1.47859 Training Accuracy: 0.49783 Validation Accuracy: 0.5082 Testing AUC: 0.81095\n",
      "Training_Statistics: \n",
      " Epoch: 22/10000 Training loss: 1.42687 Validation loss: 1.41145 Testing loss: 1.45644 Training Accuracy: 0.51109 Validation Accuracy: 0.51475 Testing AUC: 0.81649\n",
      "Training_Statistics: \n",
      " Epoch: 23/10000 Training loss: 1.39950 Validation loss: 1.39080 Testing loss: 1.43642 Training Accuracy: 0.51877 Validation Accuracy: 0.52131 Testing AUC: 0.8226\n",
      "Training_Statistics: \n",
      " Epoch: 24/10000 Training loss: 1.37519 Validation loss: 1.36980 Testing loss: 1.41722 Training Accuracy: 0.52492 Validation Accuracy: 0.5377 Testing AUC: 0.82802\n",
      "Training_Statistics: \n",
      " Epoch: 25/10000 Training loss: 1.34908 Validation loss: 1.35010 Testing loss: 1.39802 Training Accuracy: 0.5242 Validation Accuracy: 0.5377 Testing AUC: 0.83173\n",
      "Training_Statistics: \n",
      " Epoch: 26/10000 Training loss: 1.32674 Validation loss: 1.33183 Testing loss: 1.37970 Training Accuracy: 0.54481 Validation Accuracy: 0.52131 Testing AUC: 0.83583\n",
      "Training_Statistics: \n",
      " Epoch: 27/10000 Training loss: 1.29941 Validation loss: 1.31914 Testing loss: 1.36464 Training Accuracy: 0.54329 Validation Accuracy: 0.53443 Testing AUC: 0.83743\n",
      "Training_Statistics: \n",
      " Epoch: 28/10000 Training loss: 1.28021 Validation loss: 1.30505 Testing loss: 1.35329 Training Accuracy: 0.54424 Validation Accuracy: 0.53115 Testing AUC: 0.84006\n",
      "Training_Statistics: \n",
      " Epoch: 29/10000 Training loss: 1.26386 Validation loss: 1.29584 Testing loss: 1.34403 Training Accuracy: 0.55139 Validation Accuracy: 0.53443 Testing AUC: 0.84263\n",
      "Training_Statistics: \n",
      " Epoch: 30/10000 Training loss: 1.23897 Validation loss: 1.28869 Testing loss: 1.33192 Training Accuracy: 0.56222 Validation Accuracy: 0.5377 Testing AUC: 0.84385\n",
      "Training_Statistics: \n",
      " Epoch: 31/10000 Training loss: 1.22475 Validation loss: 1.28139 Testing loss: 1.32347 Training Accuracy: 0.57047 Validation Accuracy: 0.55738 Testing AUC: 0.8442\n",
      "Training_Statistics: \n",
      " Epoch: 32/10000 Training loss: 1.20388 Validation loss: 1.27781 Testing loss: 1.31586 Training Accuracy: 0.58362 Validation Accuracy: 0.56066 Testing AUC: 0.84593\n",
      "Training_Statistics: \n",
      " Epoch: 33/10000 Training loss: 1.18842 Validation loss: 1.27490 Testing loss: 1.30644 Training Accuracy: 0.59084 Validation Accuracy: 0.5541 Testing AUC: 0.84731\n",
      "Training_Statistics: \n",
      " Epoch: 34/10000 Training loss: 1.17834 Validation loss: 1.26998 Testing loss: 1.29700 Training Accuracy: 0.59698 Validation Accuracy: 0.5541 Testing AUC: 0.8491\n",
      "Training_Statistics: \n",
      " Epoch: 35/10000 Training loss: 1.15852 Validation loss: 1.27071 Testing loss: 1.28870 Training Accuracy: 0.60187 Validation Accuracy: 0.56066 Testing AUC: 0.85133\n",
      "Training_Statistics: \n",
      " Epoch: 36/10000 Training loss: 1.14257 Validation loss: 1.26766 Testing loss: 1.27899 Training Accuracy: 0.60409 Validation Accuracy: 0.54754 Testing AUC: 0.85329\n",
      "Training_Statistics: \n",
      " Epoch: 37/10000 Training loss: 1.13279 Validation loss: 1.26571 Testing loss: 1.27037 Training Accuracy: 0.61434 Validation Accuracy: 0.55082 Testing AUC: 0.85589\n",
      "Training_Statistics: \n",
      " Epoch: 38/10000 Training loss: 1.11823 Validation loss: 1.26352 Testing loss: 1.26342 Training Accuracy: 0.61727 Validation Accuracy: 0.55738 Testing AUC: 0.85845\n",
      "Training_Statistics: \n",
      " Epoch: 39/10000 Training loss: 1.10188 Validation loss: 1.25626 Testing loss: 1.25309 Training Accuracy: 0.63032 Validation Accuracy: 0.56066 Testing AUC: 0.85965\n",
      "Training_Statistics: \n",
      " Epoch: 40/10000 Training loss: 1.08645 Validation loss: 1.25897 Testing loss: 1.24229 Training Accuracy: 0.63847 Validation Accuracy: 0.56393 Testing AUC: 0.86212\n",
      "Training_Statistics: \n",
      " Epoch: 41/10000 Training loss: 1.07562 Validation loss: 1.25832 Testing loss: 1.23657 Training Accuracy: 0.64146 Validation Accuracy: 0.57049 Testing AUC: 0.86293\n",
      "Training_Statistics: \n",
      " Epoch: 42/10000 Training loss: 1.06565 Validation loss: 1.25068 Testing loss: 1.22529 Training Accuracy: 0.64413 Validation Accuracy: 0.57377 Testing AUC: 0.86397\n",
      "Training_Statistics: \n",
      " Epoch: 43/10000 Training loss: 1.04954 Validation loss: 1.24562 Testing loss: 1.21822 Training Accuracy: 0.64739 Validation Accuracy: 0.56393 Testing AUC: 0.86687\n",
      "Training_Statistics: \n",
      " Epoch: 44/10000 Training loss: 1.03781 Validation loss: 1.24499 Testing loss: 1.21462 Training Accuracy: 0.65471 Validation Accuracy: 0.56721 Testing AUC: 0.86886\n",
      "Training_Statistics: \n",
      " Epoch: 45/10000 Training loss: 1.02642 Validation loss: 1.24677 Testing loss: 1.20404 Training Accuracy: 0.65749 Validation Accuracy: 0.57049 Testing AUC: 0.86946\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training_Statistics: \n",
      " Epoch: 46/10000 Training loss: 1.01129 Validation loss: 1.24187 Testing loss: 1.19734 Training Accuracy: 0.66911 Validation Accuracy: 0.57049 Testing AUC: 0.87116\n",
      "Training_Statistics: \n",
      " Epoch: 47/10000 Training loss: 0.99917 Validation loss: 1.23896 Testing loss: 1.19257 Training Accuracy: 0.66896 Validation Accuracy: 0.57705 Testing AUC: 0.87291\n",
      "Training_Statistics: \n",
      " Epoch: 48/10000 Training loss: 0.98179 Validation loss: 1.24552 Testing loss: 1.18632 Training Accuracy: 0.67533 Validation Accuracy: 0.57705 Testing AUC: 0.87377\n",
      "Training_Statistics: \n",
      " Epoch: 49/10000 Training loss: 0.97581 Validation loss: 1.24505 Testing loss: 1.18024 Training Accuracy: 0.68426 Validation Accuracy: 0.58033 Testing AUC: 0.87479\n",
      "Training_Statistics: \n",
      " Epoch: 50/10000 Training loss: 0.96154 Validation loss: 1.24315 Testing loss: 1.17685 Training Accuracy: 0.69344 Validation Accuracy: 0.57705 Testing AUC: 0.87582\n",
      "Training_Statistics: \n",
      " Epoch: 51/10000 Training loss: 0.95315 Validation loss: 1.24322 Testing loss: 1.17147 Training Accuracy: 0.6879 Validation Accuracy: 0.58033 Testing AUC: 0.87633\n",
      "Training_Statistics: \n",
      " Epoch: 52/10000 Training loss: 0.93880 Validation loss: 1.24509 Testing loss: 1.16763 Training Accuracy: 0.69669 Validation Accuracy: 0.58361 Testing AUC: 0.87766\n",
      "Training_Statistics: \n",
      " Epoch: 53/10000 Training loss: 0.93164 Validation loss: 1.25027 Testing loss: 1.16410 Training Accuracy: 0.69679 Validation Accuracy: 0.58033 Testing AUC: 0.87807\n",
      "Training_Statistics: \n",
      " Epoch: 54/10000 Training loss: 0.91738 Validation loss: 1.25023 Testing loss: 1.16013 Training Accuracy: 0.70029 Validation Accuracy: 0.57705 Testing AUC: 0.8786\n",
      "Done Training\n",
      "Accuracy = 0.6116504854368932\n",
      "1\n",
      "Training_Statistics: \n",
      " Epoch: 1/10000 Training loss: 2.11318 Validation loss: 2.02934 Testing loss: 2.03355 Training Accuracy: 0.25871 Validation Accuracy: 0.36066 Testing AUC: 0.59464\n",
      "Training_Statistics: \n",
      " Epoch: 2/10000 Training loss: 1.99599 Validation loss: 1.93370 Testing loss: 1.93824 Training Accuracy: 0.3558 Validation Accuracy: 0.3541 Testing AUC: 0.63098\n",
      "Training_Statistics: \n",
      " Epoch: 3/10000 Training loss: 1.90436 Validation loss: 1.87095 Testing loss: 1.87652 Training Accuracy: 0.35558 Validation Accuracy: 0.3541 Testing AUC: 0.65894\n",
      "Training_Statistics: \n",
      " Epoch: 4/10000 Training loss: 1.85394 Validation loss: 1.84909 Testing loss: 1.85495 Training Accuracy: 0.35386 Validation Accuracy: 0.3541 Testing AUC: 0.68285\n",
      "Training_Statistics: \n",
      " Epoch: 5/10000 Training loss: 1.83127 Validation loss: 1.82522 Testing loss: 1.83089 Training Accuracy: 0.35472 Validation Accuracy: 0.3541 Testing AUC: 0.70365\n",
      "Training_Statistics: \n",
      " Epoch: 6/10000 Training loss: 1.80278 Validation loss: 1.79824 Testing loss: 1.80338 Training Accuracy: 0.35322 Validation Accuracy: 0.36393 Testing AUC: 0.71941\n",
      "Training_Statistics: \n",
      " Epoch: 7/10000 Training loss: 1.77539 Validation loss: 1.77650 Testing loss: 1.78098 Training Accuracy: 0.35604 Validation Accuracy: 0.37377 Testing AUC: 0.73144\n",
      "Training_Statistics: \n",
      " Epoch: 8/10000 Training loss: 1.75331 Validation loss: 1.75573 Testing loss: 1.75884 Training Accuracy: 0.38091 Validation Accuracy: 0.38361 Testing AUC: 0.74208\n",
      "Training_Statistics: \n",
      " Epoch: 9/10000 Training loss: 1.72997 Validation loss: 1.73375 Testing loss: 1.73498 Training Accuracy: 0.40423 Validation Accuracy: 0.39344 Testing AUC: 0.75206\n",
      "Training_Statistics: \n",
      " Epoch: 10/10000 Training loss: 1.70673 Validation loss: 1.71233 Testing loss: 1.71139 Training Accuracy: 0.41163 Validation Accuracy: 0.4 Testing AUC: 0.7614\n",
      "Training_Statistics: \n",
      " Epoch: 11/10000 Training loss: 1.68276 Validation loss: 1.69063 Testing loss: 1.68749 Training Accuracy: 0.41502 Validation Accuracy: 0.41967 Testing AUC: 0.77119\n",
      "Training_Statistics: \n",
      " Epoch: 12/10000 Training loss: 1.65597 Validation loss: 1.66771 Testing loss: 1.66254 Training Accuracy: 0.42038 Validation Accuracy: 0.42623 Testing AUC: 0.77946\n",
      "Training_Statistics: \n",
      " Epoch: 13/10000 Training loss: 1.63411 Validation loss: 1.64342 Testing loss: 1.63684 Training Accuracy: 0.4266 Validation Accuracy: 0.43279 Testing AUC: 0.78754\n",
      "Training_Statistics: \n",
      " Epoch: 14/10000 Training loss: 1.60354 Validation loss: 1.61883 Testing loss: 1.61107 Training Accuracy: 0.43953 Validation Accuracy: 0.4459 Testing AUC: 0.79582\n",
      "Training_Statistics: \n",
      " Epoch: 15/10000 Training loss: 1.57538 Validation loss: 1.59371 Testing loss: 1.58465 Training Accuracy: 0.44504 Validation Accuracy: 0.45246 Testing AUC: 0.80175\n",
      "Training_Statistics: \n",
      " Epoch: 16/10000 Training loss: 1.54853 Validation loss: 1.56784 Testing loss: 1.55690 Training Accuracy: 0.45075 Validation Accuracy: 0.45902 Testing AUC: 0.80639\n",
      "Training_Statistics: \n",
      " Epoch: 17/10000 Training loss: 1.51607 Validation loss: 1.54286 Testing loss: 1.52909 Training Accuracy: 0.46726 Validation Accuracy: 0.46885 Testing AUC: 0.81085\n",
      "Training_Statistics: \n",
      " Epoch: 18/10000 Training loss: 1.49157 Validation loss: 1.51924 Testing loss: 1.50237 Training Accuracy: 0.47822 Validation Accuracy: 0.48197 Testing AUC: 0.81554\n",
      "Training_Statistics: \n",
      " Epoch: 19/10000 Training loss: 1.46000 Validation loss: 1.49659 Testing loss: 1.47573 Training Accuracy: 0.48401 Validation Accuracy: 0.49836 Testing AUC: 0.82034\n",
      "Training_Statistics: \n",
      " Epoch: 20/10000 Training loss: 1.43108 Validation loss: 1.47511 Testing loss: 1.45075 Training Accuracy: 0.5017 Validation Accuracy: 0.49508 Testing AUC: 0.82495\n",
      "Training_Statistics: \n",
      " Epoch: 21/10000 Training loss: 1.40909 Validation loss: 1.45601 Testing loss: 1.42592 Training Accuracy: 0.50555 Validation Accuracy: 0.49836 Testing AUC: 0.82814\n",
      "Training_Statistics: \n",
      " Epoch: 22/10000 Training loss: 1.37993 Validation loss: 1.43837 Testing loss: 1.40150 Training Accuracy: 0.50856 Validation Accuracy: 0.50492 Testing AUC: 0.83162\n",
      "Training_Statistics: \n",
      " Epoch: 23/10000 Training loss: 1.35742 Validation loss: 1.42115 Testing loss: 1.37927 Training Accuracy: 0.51899 Validation Accuracy: 0.5082 Testing AUC: 0.8348\n",
      "Training_Statistics: \n",
      " Epoch: 24/10000 Training loss: 1.33331 Validation loss: 1.40369 Testing loss: 1.36074 Training Accuracy: 0.52092 Validation Accuracy: 0.50492 Testing AUC: 0.83812\n",
      "Training_Statistics: \n",
      " Epoch: 25/10000 Training loss: 1.31054 Validation loss: 1.38928 Testing loss: 1.34137 Training Accuracy: 0.53457 Validation Accuracy: 0.52131 Testing AUC: 0.84102\n",
      "Training_Statistics: \n",
      " Epoch: 26/10000 Training loss: 1.29058 Validation loss: 1.37759 Testing loss: 1.32313 Training Accuracy: 0.54864 Validation Accuracy: 0.52787 Testing AUC: 0.8431\n",
      "Training_Statistics: \n",
      " Epoch: 27/10000 Training loss: 1.27146 Validation loss: 1.36674 Testing loss: 1.30811 Training Accuracy: 0.55514 Validation Accuracy: 0.52131 Testing AUC: 0.84493\n",
      "Training_Statistics: \n",
      " Epoch: 28/10000 Training loss: 1.25287 Validation loss: 1.35796 Testing loss: 1.29242 Training Accuracy: 0.5624 Validation Accuracy: 0.52131 Testing AUC: 0.84676\n",
      "Training_Statistics: \n",
      " Epoch: 29/10000 Training loss: 1.23541 Validation loss: 1.35011 Testing loss: 1.27982 Training Accuracy: 0.57108 Validation Accuracy: 0.53115 Testing AUC: 0.84855\n",
      "Training_Statistics: \n",
      " Epoch: 30/10000 Training loss: 1.21880 Validation loss: 1.34209 Testing loss: 1.26974 Training Accuracy: 0.57436 Validation Accuracy: 0.54426 Testing AUC: 0.84994\n",
      "Training_Statistics: \n",
      " Epoch: 31/10000 Training loss: 1.20159 Validation loss: 1.33779 Testing loss: 1.26061 Training Accuracy: 0.59094 Validation Accuracy: 0.54426 Testing AUC: 0.85029\n",
      "Training_Statistics: \n",
      " Epoch: 32/10000 Training loss: 1.18353 Validation loss: 1.32979 Testing loss: 1.25333 Training Accuracy: 0.59566 Validation Accuracy: 0.55738 Testing AUC: 0.85244\n",
      "Training_Statistics: \n",
      " Epoch: 33/10000 Training loss: 1.17301 Validation loss: 1.32498 Testing loss: 1.24620 Training Accuracy: 0.60833 Validation Accuracy: 0.56066 Testing AUC: 0.85374\n",
      "Training_Statistics: \n",
      " Epoch: 34/10000 Training loss: 1.15044 Validation loss: 1.32070 Testing loss: 1.23862 Training Accuracy: 0.61777 Validation Accuracy: 0.57049 Testing AUC: 0.85452\n",
      "Training_Statistics: \n",
      " Epoch: 35/10000 Training loss: 1.13959 Validation loss: 1.31548 Testing loss: 1.23238 Training Accuracy: 0.6241 Validation Accuracy: 0.56721 Testing AUC: 0.85549\n",
      "Training_Statistics: \n",
      " Epoch: 36/10000 Training loss: 1.12757 Validation loss: 1.31152 Testing loss: 1.22567 Training Accuracy: 0.62534 Validation Accuracy: 0.56393 Testing AUC: 0.85684\n",
      "Training_Statistics: \n",
      " Epoch: 37/10000 Training loss: 1.10763 Validation loss: 1.30609 Testing loss: 1.22046 Training Accuracy: 0.63245 Validation Accuracy: 0.56721 Testing AUC: 0.85774\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training_Statistics: \n",
      " Epoch: 38/10000 Training loss: 1.09756 Validation loss: 1.30374 Testing loss: 1.21437 Training Accuracy: 0.63191 Validation Accuracy: 0.56721 Testing AUC: 0.85944\n",
      "Training_Statistics: \n",
      " Epoch: 39/10000 Training loss: 1.08138 Validation loss: 1.29949 Testing loss: 1.20913 Training Accuracy: 0.6362 Validation Accuracy: 0.56721 Testing AUC: 0.86128\n",
      "Training_Statistics: \n",
      " Epoch: 40/10000 Training loss: 1.06705 Validation loss: 1.29751 Testing loss: 1.20218 Training Accuracy: 0.63681 Validation Accuracy: 0.56066 Testing AUC: 0.86195\n",
      "Training_Statistics: \n",
      " Epoch: 41/10000 Training loss: 1.04866 Validation loss: 1.29643 Testing loss: 1.19648 Training Accuracy: 0.64743 Validation Accuracy: 0.55738 Testing AUC: 0.86343\n",
      "Training_Statistics: \n",
      " Epoch: 42/10000 Training loss: 1.03746 Validation loss: 1.29541 Testing loss: 1.19290 Training Accuracy: 0.65132 Validation Accuracy: 0.55738 Testing AUC: 0.86494\n",
      "Training_Statistics: \n",
      " Epoch: 43/10000 Training loss: 1.02681 Validation loss: 1.29314 Testing loss: 1.18721 Training Accuracy: 0.65664 Validation Accuracy: 0.55738 Testing AUC: 0.86559\n",
      "Training_Statistics: \n",
      " Epoch: 44/10000 Training loss: 1.01504 Validation loss: 1.29088 Testing loss: 1.18422 Training Accuracy: 0.65692 Validation Accuracy: 0.56393 Testing AUC: 0.86601\n",
      "Training_Statistics: \n",
      " Epoch: 45/10000 Training loss: 1.00379 Validation loss: 1.29120 Testing loss: 1.18232 Training Accuracy: 0.66903 Validation Accuracy: 0.56066 Testing AUC: 0.86737\n",
      "Training_Statistics: \n",
      " Epoch: 46/10000 Training loss: 0.98687 Validation loss: 1.29318 Testing loss: 1.17605 Training Accuracy: 0.67761 Validation Accuracy: 0.58033 Testing AUC: 0.86751\n",
      "Training_Statistics: \n",
      " Epoch: 47/10000 Training loss: 0.97455 Validation loss: 1.29082 Testing loss: 1.17569 Training Accuracy: 0.6835 Validation Accuracy: 0.55738 Testing AUC: 0.86883\n",
      "Training_Statistics: \n",
      " Epoch: 48/10000 Training loss: 0.96281 Validation loss: 1.28691 Testing loss: 1.17353 Training Accuracy: 0.68743 Validation Accuracy: 0.57377 Testing AUC: 0.86941\n",
      "Training_Statistics: \n",
      " Epoch: 49/10000 Training loss: 0.94985 Validation loss: 1.28870 Testing loss: 1.16876 Training Accuracy: 0.69022 Validation Accuracy: 0.57049 Testing AUC: 0.87007\n",
      "Training_Statistics: \n",
      " Epoch: 50/10000 Training loss: 0.93304 Validation loss: 1.29173 Testing loss: 1.16392 Training Accuracy: 0.69494 Validation Accuracy: 0.5541 Testing AUC: 0.87141\n",
      "Training_Statistics: \n",
      " Epoch: 51/10000 Training loss: 0.92499 Validation loss: 1.29232 Testing loss: 1.16272 Training Accuracy: 0.7004 Validation Accuracy: 0.5541 Testing AUC: 0.87178\n",
      "Training_Statistics: \n",
      " Epoch: 52/10000 Training loss: 0.91000 Validation loss: 1.29171 Testing loss: 1.16356 Training Accuracy: 0.70398 Validation Accuracy: 0.55082 Testing AUC: 0.87267\n",
      "Training_Statistics: \n",
      " Epoch: 53/10000 Training loss: 0.90253 Validation loss: 1.29329 Testing loss: 1.16324 Training Accuracy: 0.70405 Validation Accuracy: 0.55738 Testing AUC: 0.87274\n",
      "Training_Statistics: \n",
      " Epoch: 54/10000 Training loss: 0.89025 Validation loss: 1.29810 Testing loss: 1.15803 Training Accuracy: 0.71026 Validation Accuracy: 0.5541 Testing AUC: 0.87309\n",
      "Training_Statistics: \n",
      " Epoch: 55/10000 Training loss: 0.87624 Validation loss: 1.29970 Testing loss: 1.15983 Training Accuracy: 0.71276 Validation Accuracy: 0.5541 Testing AUC: 0.87379\n",
      "Done Training\n",
      "Accuracy = 0.6067961165048543\n",
      "2\n",
      "Training_Statistics: \n",
      " Epoch: 1/10000 Training loss: 2.16061 Validation loss: 2.06897 Testing loss: 2.07703 Training Accuracy: 0.19187 Validation Accuracy: 0.35082 Testing AUC: 0.50496\n",
      "Training_Statistics: \n",
      " Epoch: 2/10000 Training loss: 2.03582 Validation loss: 1.95317 Testing loss: 1.96586 Training Accuracy: 0.34286 Validation Accuracy: 0.34754 Testing AUC: 0.53138\n",
      "Training_Statistics: \n",
      " Epoch: 3/10000 Training loss: 1.92808 Validation loss: 1.88183 Testing loss: 1.89845 Training Accuracy: 0.35787 Validation Accuracy: 0.3541 Testing AUC: 0.55802\n",
      "Training_Statistics: \n",
      " Epoch: 4/10000 Training loss: 1.87561 Validation loss: 1.86928 Testing loss: 1.88923 Training Accuracy: 0.35504 Validation Accuracy: 0.3541 Testing AUC: 0.59126\n",
      "Training_Statistics: \n",
      " Epoch: 5/10000 Training loss: 1.86145 Validation loss: 1.84386 Testing loss: 1.86456 Training Accuracy: 0.35311 Validation Accuracy: 0.3541 Testing AUC: 0.6249\n",
      "Training_Statistics: \n",
      " Epoch: 6/10000 Training loss: 1.83147 Validation loss: 1.81053 Testing loss: 1.83016 Training Accuracy: 0.35694 Validation Accuracy: 0.36066 Testing AUC: 0.65193\n",
      "Training_Statistics: \n",
      " Epoch: 7/10000 Training loss: 1.79802 Validation loss: 1.78884 Testing loss: 1.80762 Training Accuracy: 0.36315 Validation Accuracy: 0.37377 Testing AUC: 0.67131\n",
      "Training_Statistics: \n",
      " Epoch: 8/10000 Training loss: 1.77865 Validation loss: 1.77254 Testing loss: 1.79076 Training Accuracy: 0.3802 Validation Accuracy: 0.37049 Testing AUC: 0.68724\n",
      "Training_Statistics: \n",
      " Epoch: 9/10000 Training loss: 1.76172 Validation loss: 1.75415 Testing loss: 1.77211 Training Accuracy: 0.39678 Validation Accuracy: 0.39016 Testing AUC: 0.7014\n",
      "Training_Statistics: \n",
      " Epoch: 10/10000 Training loss: 1.74189 Validation loss: 1.73246 Testing loss: 1.75125 Training Accuracy: 0.40363 Validation Accuracy: 0.4 Testing AUC: 0.71212\n",
      "Training_Statistics: \n",
      " Epoch: 11/10000 Training loss: 1.72045 Validation loss: 1.71048 Testing loss: 1.73025 Training Accuracy: 0.40034 Validation Accuracy: 0.39016 Testing AUC: 0.72026\n",
      "Training_Statistics: \n",
      " Epoch: 12/10000 Training loss: 1.69505 Validation loss: 1.68950 Testing loss: 1.71022 Training Accuracy: 0.40141 Validation Accuracy: 0.39016 Testing AUC: 0.72939\n",
      "Training_Statistics: \n",
      " Epoch: 13/10000 Training loss: 1.67375 Validation loss: 1.66730 Testing loss: 1.68865 Training Accuracy: 0.40506 Validation Accuracy: 0.40328 Testing AUC: 0.73935\n",
      "Training_Statistics: \n",
      " Epoch: 14/10000 Training loss: 1.64844 Validation loss: 1.64319 Testing loss: 1.66492 Training Accuracy: 0.4261 Validation Accuracy: 0.43279 Testing AUC: 0.75114\n",
      "Training_Statistics: \n",
      " Epoch: 15/10000 Training loss: 1.62104 Validation loss: 1.61914 Testing loss: 1.64039 Training Accuracy: 0.44579 Validation Accuracy: 0.45246 Testing AUC: 0.7614\n",
      "Training_Statistics: \n",
      " Epoch: 16/10000 Training loss: 1.59488 Validation loss: 1.59490 Testing loss: 1.61565 Training Accuracy: 0.44703 Validation Accuracy: 0.46885 Testing AUC: 0.76995\n",
      "Training_Statistics: \n",
      " Epoch: 17/10000 Training loss: 1.56668 Validation loss: 1.56965 Testing loss: 1.59063 Training Accuracy: 0.45157 Validation Accuracy: 0.48525 Testing AUC: 0.77695\n",
      "Training_Statistics: \n",
      " Epoch: 18/10000 Training loss: 1.53504 Validation loss: 1.54367 Testing loss: 1.56579 Training Accuracy: 0.46794 Validation Accuracy: 0.49508 Testing AUC: 0.78239\n",
      "Training_Statistics: \n",
      " Epoch: 19/10000 Training loss: 1.50478 Validation loss: 1.51801 Testing loss: 1.54217 Training Accuracy: 0.49013 Validation Accuracy: 0.5082 Testing AUC: 0.78673\n",
      "Training_Statistics: \n",
      " Epoch: 20/10000 Training loss: 1.47722 Validation loss: 1.49362 Testing loss: 1.52055 Training Accuracy: 0.49995 Validation Accuracy: 0.52459 Testing AUC: 0.78901\n",
      "Training_Statistics: \n",
      " Epoch: 21/10000 Training loss: 1.45039 Validation loss: 1.46993 Testing loss: 1.49928 Training Accuracy: 0.5082 Validation Accuracy: 0.50492 Testing AUC: 0.79205\n",
      "Training_Statistics: \n",
      " Epoch: 22/10000 Training loss: 1.41898 Validation loss: 1.44750 Testing loss: 1.47856 Training Accuracy: 0.51334 Validation Accuracy: 0.50164 Testing AUC: 0.79511\n",
      "Training_Statistics: \n",
      " Epoch: 23/10000 Training loss: 1.38706 Validation loss: 1.42700 Testing loss: 1.45836 Training Accuracy: 0.51031 Validation Accuracy: 0.5082 Testing AUC: 0.79912\n",
      "Training_Statistics: \n",
      " Epoch: 24/10000 Training loss: 1.35826 Validation loss: 1.40833 Testing loss: 1.44012 Training Accuracy: 0.51664 Validation Accuracy: 0.50164 Testing AUC: 0.80265\n",
      "Training_Statistics: \n",
      " Epoch: 25/10000 Training loss: 1.33369 Validation loss: 1.39231 Testing loss: 1.42539 Training Accuracy: 0.52321 Validation Accuracy: 0.51148 Testing AUC: 0.80511\n",
      "Training_Statistics: \n",
      " Epoch: 26/10000 Training loss: 1.31122 Validation loss: 1.37653 Testing loss: 1.41179 Training Accuracy: 0.53621 Validation Accuracy: 0.5082 Testing AUC: 0.808\n",
      "Training_Statistics: \n",
      " Epoch: 27/10000 Training loss: 1.28981 Validation loss: 1.36150 Testing loss: 1.39871 Training Accuracy: 0.54364 Validation Accuracy: 0.51148 Testing AUC: 0.81167\n",
      "Training_Statistics: \n",
      " Epoch: 28/10000 Training loss: 1.27098 Validation loss: 1.34924 Testing loss: 1.38763 Training Accuracy: 0.55228 Validation Accuracy: 0.52787 Testing AUC: 0.81532\n",
      "Training_Statistics: \n",
      " Epoch: 29/10000 Training loss: 1.24323 Validation loss: 1.33851 Testing loss: 1.37519 Training Accuracy: 0.56769 Validation Accuracy: 0.52787 Testing AUC: 0.81764\n",
      "Training_Statistics: \n",
      " Epoch: 30/10000 Training loss: 1.22805 Validation loss: 1.32836 Testing loss: 1.36470 Training Accuracy: 0.5734 Validation Accuracy: 0.53443 Testing AUC: 0.81999\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training_Statistics: \n",
      " Epoch: 31/10000 Training loss: 1.20897 Validation loss: 1.31894 Testing loss: 1.35475 Training Accuracy: 0.58812 Validation Accuracy: 0.53443 Testing AUC: 0.82417\n",
      "Training_Statistics: \n",
      " Epoch: 32/10000 Training loss: 1.19323 Validation loss: 1.31197 Testing loss: 1.34878 Training Accuracy: 0.59651 Validation Accuracy: 0.52459 Testing AUC: 0.82448\n",
      "Training_Statistics: \n",
      " Epoch: 33/10000 Training loss: 1.17103 Validation loss: 1.30440 Testing loss: 1.33698 Training Accuracy: 0.6017 Validation Accuracy: 0.52459 Testing AUC: 0.82612\n",
      "Training_Statistics: \n",
      " Epoch: 34/10000 Training loss: 1.15801 Validation loss: 1.29641 Testing loss: 1.32415 Training Accuracy: 0.60948 Validation Accuracy: 0.52787 Testing AUC: 0.82926\n",
      "Training_Statistics: \n",
      " Epoch: 35/10000 Training loss: 1.14119 Validation loss: 1.29235 Testing loss: 1.31576 Training Accuracy: 0.61745 Validation Accuracy: 0.53115 Testing AUC: 0.83103\n",
      "Training_Statistics: \n",
      " Epoch: 36/10000 Training loss: 1.11820 Validation loss: 1.29470 Testing loss: 1.31839 Training Accuracy: 0.62899 Validation Accuracy: 0.51803 Testing AUC: 0.83032\n",
      "Training_Statistics: \n",
      " Epoch: 37/10000 Training loss: 1.10815 Validation loss: 1.28947 Testing loss: 1.30898 Training Accuracy: 0.63049 Validation Accuracy: 0.51475 Testing AUC: 0.83228\n",
      "Training_Statistics: \n",
      " Epoch: 38/10000 Training loss: 1.09082 Validation loss: 1.28360 Testing loss: 1.29816 Training Accuracy: 0.63764 Validation Accuracy: 0.54098 Testing AUC: 0.83617\n",
      "Training_Statistics: \n",
      " Epoch: 39/10000 Training loss: 1.07914 Validation loss: 1.28266 Testing loss: 1.29503 Training Accuracy: 0.64739 Validation Accuracy: 0.51803 Testing AUC: 0.83515\n",
      "Training_Statistics: \n",
      " Epoch: 40/10000 Training loss: 1.06066 Validation loss: 1.28179 Testing loss: 1.29153 Training Accuracy: 0.64885 Validation Accuracy: 0.53115 Testing AUC: 0.83605\n",
      "Training_Statistics: \n",
      " Epoch: 41/10000 Training loss: 1.04746 Validation loss: 1.27881 Testing loss: 1.28059 Training Accuracy: 0.65132 Validation Accuracy: 0.52459 Testing AUC: 0.83935\n",
      "Training_Statistics: \n",
      " Epoch: 42/10000 Training loss: 1.03423 Validation loss: 1.27763 Testing loss: 1.27487 Training Accuracy: 0.65993 Validation Accuracy: 0.52131 Testing AUC: 0.84093\n",
      "Training_Statistics: \n",
      " Epoch: 43/10000 Training loss: 1.01909 Validation loss: 1.28020 Testing loss: 1.27854 Training Accuracy: 0.66525 Validation Accuracy: 0.52787 Testing AUC: 0.84024\n",
      "Training_Statistics: \n",
      " Epoch: 44/10000 Training loss: 1.00470 Validation loss: 1.27983 Testing loss: 1.27465 Training Accuracy: 0.66736 Validation Accuracy: 0.52787 Testing AUC: 0.84095\n",
      "Training_Statistics: \n",
      " Epoch: 45/10000 Training loss: 0.99387 Validation loss: 1.27680 Testing loss: 1.26495 Training Accuracy: 0.67164 Validation Accuracy: 0.53115 Testing AUC: 0.84359\n",
      "Training_Statistics: \n",
      " Epoch: 46/10000 Training loss: 0.98205 Validation loss: 1.27522 Testing loss: 1.26184 Training Accuracy: 0.6784 Validation Accuracy: 0.53443 Testing AUC: 0.84509\n",
      "Training_Statistics: \n",
      " Epoch: 47/10000 Training loss: 0.96917 Validation loss: 1.28319 Testing loss: 1.27000 Training Accuracy: 0.68257 Validation Accuracy: 0.54426 Testing AUC: 0.84417\n",
      "Training_Statistics: \n",
      " Epoch: 48/10000 Training loss: 0.95587 Validation loss: 1.27776 Testing loss: 1.25767 Training Accuracy: 0.68029 Validation Accuracy: 0.54754 Testing AUC: 0.847\n",
      "Training_Statistics: \n",
      " Epoch: 49/10000 Training loss: 0.93990 Validation loss: 1.27644 Testing loss: 1.25710 Training Accuracy: 0.68872 Validation Accuracy: 0.54754 Testing AUC: 0.84822\n",
      "Training_Statistics: \n",
      " Epoch: 50/10000 Training loss: 0.93075 Validation loss: 1.27484 Testing loss: 1.25511 Training Accuracy: 0.69715 Validation Accuracy: 0.54754 Testing AUC: 0.84868\n",
      "Training_Statistics: \n",
      " Epoch: 51/10000 Training loss: 0.91611 Validation loss: 1.27850 Testing loss: 1.25645 Training Accuracy: 0.70148 Validation Accuracy: 0.55082 Testing AUC: 0.84895\n",
      "Training_Statistics: \n",
      " Epoch: 52/10000 Training loss: 0.90774 Validation loss: 1.28020 Testing loss: 1.25316 Training Accuracy: 0.70119 Validation Accuracy: 0.54754 Testing AUC: 0.85072\n",
      "Training_Statistics: \n",
      " Epoch: 53/10000 Training loss: 0.89276 Validation loss: 1.28062 Testing loss: 1.25514 Training Accuracy: 0.70748 Validation Accuracy: 0.55082 Testing AUC: 0.85056\n",
      "Done Training\n",
      "Accuracy = 0.5900755124056095\n",
      "3\n",
      "Training_Statistics: \n",
      " Epoch: 1/10000 Training loss: 2.21556 Validation loss: 2.11378 Testing loss: 2.11989 Training Accuracy: 0.10261 Validation Accuracy: 0.20328 Testing AUC: 0.4669\n",
      "Training_Statistics: \n",
      " Epoch: 2/10000 Training loss: 2.08757 Validation loss: 1.99470 Testing loss: 2.00316 Training Accuracy: 0.24249 Validation Accuracy: 0.36066 Testing AUC: 0.50142\n",
      "Training_Statistics: \n",
      " Epoch: 3/10000 Training loss: 1.97476 Validation loss: 1.90096 Testing loss: 1.91366 Training Accuracy: 0.349 Validation Accuracy: 0.3541 Testing AUC: 0.52908\n",
      "Training_Statistics: \n",
      " Epoch: 4/10000 Training loss: 1.89204 Validation loss: 1.86540 Testing loss: 1.88353 Training Accuracy: 0.35515 Validation Accuracy: 0.3541 Testing AUC: 0.55509\n",
      "Training_Statistics: \n",
      " Epoch: 5/10000 Training loss: 1.87018 Validation loss: 1.85616 Testing loss: 1.87925 Training Accuracy: 0.35375 Validation Accuracy: 0.3541 Testing AUC: 0.5861\n",
      "Training_Statistics: \n",
      " Epoch: 6/10000 Training loss: 1.84920 Validation loss: 1.81919 Testing loss: 1.84510 Training Accuracy: 0.35504 Validation Accuracy: 0.3541 Testing AUC: 0.61547\n",
      "Training_Statistics: \n",
      " Epoch: 7/10000 Training loss: 1.81512 Validation loss: 1.78620 Testing loss: 1.81408 Training Accuracy: 0.35343 Validation Accuracy: 0.3541 Testing AUC: 0.64167\n",
      "Training_Statistics: \n",
      " Epoch: 8/10000 Training loss: 1.78644 Validation loss: 1.76861 Testing loss: 1.79855 Training Accuracy: 0.35361 Validation Accuracy: 0.39672 Testing AUC: 0.66205\n",
      "Training_Statistics: \n",
      " Epoch: 9/10000 Training loss: 1.76644 Validation loss: 1.75499 Testing loss: 1.78687 Training Accuracy: 0.40521 Validation Accuracy: 0.44262 Testing AUC: 0.68024\n",
      "Training_Statistics: \n",
      " Epoch: 10/10000 Training loss: 1.74805 Validation loss: 1.73670 Testing loss: 1.77071 Training Accuracy: 0.43762 Validation Accuracy: 0.45246 Testing AUC: 0.69485\n",
      "Training_Statistics: \n",
      " Epoch: 11/10000 Training loss: 1.72746 Validation loss: 1.71236 Testing loss: 1.74932 Training Accuracy: 0.44018 Validation Accuracy: 0.4459 Testing AUC: 0.7099\n",
      "Training_Statistics: \n",
      " Epoch: 12/10000 Training loss: 1.70435 Validation loss: 1.68692 Testing loss: 1.72758 Training Accuracy: 0.4411 Validation Accuracy: 0.4459 Testing AUC: 0.72149\n",
      "Training_Statistics: \n",
      " Epoch: 13/10000 Training loss: 1.67864 Validation loss: 1.66370 Testing loss: 1.70841 Training Accuracy: 0.43746 Validation Accuracy: 0.43934 Testing AUC: 0.7312\n",
      "Training_Statistics: \n",
      " Epoch: 14/10000 Training loss: 1.65364 Validation loss: 1.64183 Testing loss: 1.69044 Training Accuracy: 0.43335 Validation Accuracy: 0.43607 Testing AUC: 0.73982\n",
      "Training_Statistics: \n",
      " Epoch: 15/10000 Training loss: 1.63035 Validation loss: 1.61831 Testing loss: 1.66983 Training Accuracy: 0.43285 Validation Accuracy: 0.44262 Testing AUC: 0.74863\n",
      "Training_Statistics: \n",
      " Epoch: 16/10000 Training loss: 1.60921 Validation loss: 1.59303 Testing loss: 1.64600 Training Accuracy: 0.43599 Validation Accuracy: 0.45246 Testing AUC: 0.75601\n",
      "Training_Statistics: \n",
      " Epoch: 17/10000 Training loss: 1.57827 Validation loss: 1.56838 Testing loss: 1.62196 Training Accuracy: 0.4495 Validation Accuracy: 0.4459 Testing AUC: 0.76234\n",
      "Training_Statistics: \n",
      " Epoch: 18/10000 Training loss: 1.54973 Validation loss: 1.54543 Testing loss: 1.59996 Training Accuracy: 0.45622 Validation Accuracy: 0.45246 Testing AUC: 0.7683\n",
      "Training_Statistics: \n",
      " Epoch: 19/10000 Training loss: 1.52410 Validation loss: 1.52201 Testing loss: 1.57796 Training Accuracy: 0.4675 Validation Accuracy: 0.47213 Testing AUC: 0.77355\n",
      "Training_Statistics: \n",
      " Epoch: 20/10000 Training loss: 1.49562 Validation loss: 1.49719 Testing loss: 1.55538 Training Accuracy: 0.48598 Validation Accuracy: 0.48197 Testing AUC: 0.77689\n",
      "Training_Statistics: \n",
      " Epoch: 21/10000 Training loss: 1.46865 Validation loss: 1.47248 Testing loss: 1.53360 Training Accuracy: 0.50205 Validation Accuracy: 0.48525 Testing AUC: 0.7805\n",
      "Training_Statistics: \n",
      " Epoch: 22/10000 Training loss: 1.43484 Validation loss: 1.45027 Testing loss: 1.51476 Training Accuracy: 0.50845 Validation Accuracy: 0.48852 Testing AUC: 0.78275\n",
      "Training_Statistics: \n",
      " Epoch: 23/10000 Training loss: 1.40860 Validation loss: 1.42981 Testing loss: 1.49691 Training Accuracy: 0.51416 Validation Accuracy: 0.48852 Testing AUC: 0.78655\n",
      "Training_Statistics: \n",
      " Epoch: 24/10000 Training loss: 1.38651 Validation loss: 1.41093 Testing loss: 1.47980 Training Accuracy: 0.5157 Validation Accuracy: 0.49836 Testing AUC: 0.79099\n",
      "Training_Statistics: \n",
      " Epoch: 25/10000 Training loss: 1.35722 Validation loss: 1.39455 Testing loss: 1.46640 Training Accuracy: 0.51953 Validation Accuracy: 0.4918 Testing AUC: 0.79527\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training_Statistics: \n",
      " Epoch: 26/10000 Training loss: 1.33586 Validation loss: 1.37886 Testing loss: 1.45535 Training Accuracy: 0.52417 Validation Accuracy: 0.4918 Testing AUC: 0.7988\n",
      "Training_Statistics: \n",
      " Epoch: 27/10000 Training loss: 1.31396 Validation loss: 1.36460 Testing loss: 1.44694 Training Accuracy: 0.53042 Validation Accuracy: 0.49836 Testing AUC: 0.79994\n",
      "Training_Statistics: \n",
      " Epoch: 28/10000 Training loss: 1.29693 Validation loss: 1.35306 Testing loss: 1.43833 Training Accuracy: 0.54161 Validation Accuracy: 0.51475 Testing AUC: 0.80228\n",
      "Training_Statistics: \n",
      " Epoch: 29/10000 Training loss: 1.27531 Validation loss: 1.34327 Testing loss: 1.43026 Training Accuracy: 0.55104 Validation Accuracy: 0.52787 Testing AUC: 0.80412\n",
      "Training_Statistics: \n",
      " Epoch: 30/10000 Training loss: 1.25326 Validation loss: 1.33434 Testing loss: 1.42295 Training Accuracy: 0.56158 Validation Accuracy: 0.53443 Testing AUC: 0.80637\n",
      "Training_Statistics: \n",
      " Epoch: 31/10000 Training loss: 1.23543 Validation loss: 1.32676 Testing loss: 1.41546 Training Accuracy: 0.57519 Validation Accuracy: 0.53115 Testing AUC: 0.8079\n",
      "Training_Statistics: \n",
      " Epoch: 32/10000 Training loss: 1.21603 Validation loss: 1.32152 Testing loss: 1.40844 Training Accuracy: 0.58037 Validation Accuracy: 0.54098 Testing AUC: 0.80907\n",
      "Training_Statistics: \n",
      " Epoch: 33/10000 Training loss: 1.19969 Validation loss: 1.31587 Testing loss: 1.40076 Training Accuracy: 0.58129 Validation Accuracy: 0.52787 Testing AUC: 0.80989\n",
      "Training_Statistics: \n",
      " Epoch: 34/10000 Training loss: 1.18508 Validation loss: 1.31037 Testing loss: 1.39260 Training Accuracy: 0.58637 Validation Accuracy: 0.52459 Testing AUC: 0.81187\n",
      "Training_Statistics: \n",
      " Epoch: 35/10000 Training loss: 1.16873 Validation loss: 1.30581 Testing loss: 1.38578 Training Accuracy: 0.59826 Validation Accuracy: 0.5377 Testing AUC: 0.81194\n",
      "Training_Statistics: \n",
      " Epoch: 36/10000 Training loss: 1.14837 Validation loss: 1.30147 Testing loss: 1.37862 Training Accuracy: 0.60337 Validation Accuracy: 0.54754 Testing AUC: 0.81268\n",
      "Training_Statistics: \n",
      " Epoch: 37/10000 Training loss: 1.13352 Validation loss: 1.29643 Testing loss: 1.37201 Training Accuracy: 0.61777 Validation Accuracy: 0.54754 Testing AUC: 0.81459\n",
      "Training_Statistics: \n",
      " Epoch: 38/10000 Training loss: 1.11634 Validation loss: 1.29204 Testing loss: 1.36693 Training Accuracy: 0.62881 Validation Accuracy: 0.5541 Testing AUC: 0.81516\n",
      "Training_Statistics: \n",
      " Epoch: 39/10000 Training loss: 1.10525 Validation loss: 1.28894 Testing loss: 1.36132 Training Accuracy: 0.62623 Validation Accuracy: 0.56721 Testing AUC: 0.81588\n",
      "Training_Statistics: \n",
      " Epoch: 40/10000 Training loss: 1.08472 Validation loss: 1.28511 Testing loss: 1.35650 Training Accuracy: 0.63252 Validation Accuracy: 0.55738 Testing AUC: 0.81795\n",
      "Training_Statistics: \n",
      " Epoch: 41/10000 Training loss: 1.06979 Validation loss: 1.27975 Testing loss: 1.35316 Training Accuracy: 0.64171 Validation Accuracy: 0.56393 Testing AUC: 0.81928\n",
      "Training_Statistics: \n",
      " Epoch: 42/10000 Training loss: 1.05269 Validation loss: 1.27450 Testing loss: 1.34944 Training Accuracy: 0.64149 Validation Accuracy: 0.56393 Testing AUC: 0.81977\n",
      "Training_Statistics: \n",
      " Epoch: 43/10000 Training loss: 1.03992 Validation loss: 1.27096 Testing loss: 1.34686 Training Accuracy: 0.64628 Validation Accuracy: 0.56393 Testing AUC: 0.82037\n",
      "Training_Statistics: \n",
      " Epoch: 44/10000 Training loss: 1.02246 Validation loss: 1.26855 Testing loss: 1.34520 Training Accuracy: 0.65296 Validation Accuracy: 0.55082 Testing AUC: 0.82052\n",
      "Training_Statistics: \n",
      " Epoch: 45/10000 Training loss: 1.01267 Validation loss: 1.26120 Testing loss: 1.34550 Training Accuracy: 0.65392 Validation Accuracy: 0.55082 Testing AUC: 0.82251\n",
      "Training_Statistics: \n",
      " Epoch: 46/10000 Training loss: 0.99394 Validation loss: 1.25824 Testing loss: 1.34542 Training Accuracy: 0.66318 Validation Accuracy: 0.55082 Testing AUC: 0.82348\n",
      "Training_Statistics: \n",
      " Epoch: 47/10000 Training loss: 0.97981 Validation loss: 1.25899 Testing loss: 1.34537 Training Accuracy: 0.66918 Validation Accuracy: 0.56393 Testing AUC: 0.8227\n",
      "Training_Statistics: \n",
      " Epoch: 48/10000 Training loss: 0.96698 Validation loss: 1.25109 Testing loss: 1.34670 Training Accuracy: 0.67386 Validation Accuracy: 0.56393 Testing AUC: 0.82307\n",
      "Training_Statistics: \n",
      " Epoch: 49/10000 Training loss: 0.95062 Validation loss: 1.24727 Testing loss: 1.34950 Training Accuracy: 0.68326 Validation Accuracy: 0.56393 Testing AUC: 0.82315\n",
      "Training_Statistics: \n",
      " Epoch: 50/10000 Training loss: 0.93783 Validation loss: 1.25228 Testing loss: 1.35000 Training Accuracy: 0.68747 Validation Accuracy: 0.55082 Testing AUC: 0.82387\n",
      "Training_Statistics: \n",
      " Epoch: 51/10000 Training loss: 0.92395 Validation loss: 1.24301 Testing loss: 1.35035 Training Accuracy: 0.69444 Validation Accuracy: 0.56066 Testing AUC: 0.82505\n",
      "Training_Statistics: \n",
      " Epoch: 52/10000 Training loss: 0.91446 Validation loss: 1.24435 Testing loss: 1.35484 Training Accuracy: 0.69276 Validation Accuracy: 0.57049 Testing AUC: 0.82551\n",
      "Training_Statistics: \n",
      " Epoch: 53/10000 Training loss: 0.89661 Validation loss: 1.24618 Testing loss: 1.35281 Training Accuracy: 0.70448 Validation Accuracy: 0.55738 Testing AUC: 0.82513\n",
      "Training_Statistics: \n",
      " Epoch: 54/10000 Training loss: 0.88754 Validation loss: 1.23913 Testing loss: 1.35587 Training Accuracy: 0.70469 Validation Accuracy: 0.56721 Testing AUC: 0.82656\n",
      "Training_Statistics: \n",
      " Epoch: 55/10000 Training loss: 0.87101 Validation loss: 1.23479 Testing loss: 1.36112 Training Accuracy: 0.71359 Validation Accuracy: 0.58361 Testing AUC: 0.82756\n",
      "Training_Statistics: \n",
      " Epoch: 56/10000 Training loss: 0.86020 Validation loss: 1.23508 Testing loss: 1.36079 Training Accuracy: 0.7218 Validation Accuracy: 0.57705 Testing AUC: 0.82851\n",
      "Training_Statistics: \n",
      " Epoch: 57/10000 Training loss: 0.84394 Validation loss: 1.23535 Testing loss: 1.36383 Training Accuracy: 0.72538 Validation Accuracy: 0.56721 Testing AUC: 0.82785\n",
      "Training_Statistics: \n",
      " Epoch: 58/10000 Training loss: 0.83060 Validation loss: 1.23299 Testing loss: 1.36911 Training Accuracy: 0.72602 Validation Accuracy: 0.57377 Testing AUC: 0.8288\n",
      "Training_Statistics: \n",
      " Epoch: 59/10000 Training loss: 0.81952 Validation loss: 1.23935 Testing loss: 1.37671 Training Accuracy: 0.72984 Validation Accuracy: 0.56393 Testing AUC: 0.8284\n",
      "Training_Statistics: \n",
      " Epoch: 60/10000 Training loss: 0.80579 Validation loss: 1.23417 Testing loss: 1.37421 Training Accuracy: 0.73639 Validation Accuracy: 0.56066 Testing AUC: 0.82891\n",
      "Training_Statistics: \n",
      " Epoch: 61/10000 Training loss: 0.79827 Validation loss: 1.23315 Testing loss: 1.38208 Training Accuracy: 0.73703 Validation Accuracy: 0.57705 Testing AUC: 0.82978\n",
      "Training_Statistics: \n",
      " Epoch: 62/10000 Training loss: 0.78109 Validation loss: 1.23546 Testing loss: 1.38513 Training Accuracy: 0.7461 Validation Accuracy: 0.56721 Testing AUC: 0.83058\n",
      "Training_Statistics: \n",
      " Epoch: 63/10000 Training loss: 0.77404 Validation loss: 1.23181 Testing loss: 1.38316 Training Accuracy: 0.7438 Validation Accuracy: 0.58033 Testing AUC: 0.83022\n",
      "Training_Statistics: \n",
      " Epoch: 64/10000 Training loss: 0.76124 Validation loss: 1.23768 Testing loss: 1.39552 Training Accuracy: 0.75128 Validation Accuracy: 0.58033 Testing AUC: 0.83004\n",
      "Training_Statistics: \n",
      " Epoch: 65/10000 Training loss: 0.75260 Validation loss: 1.23313 Testing loss: 1.39317 Training Accuracy: 0.75181 Validation Accuracy: 0.57705 Testing AUC: 0.83162\n",
      "Training_Statistics: \n",
      " Epoch: 66/10000 Training loss: 0.73998 Validation loss: 1.23959 Testing loss: 1.40037 Training Accuracy: 0.75456 Validation Accuracy: 0.59016 Testing AUC: 0.83204\n",
      "Training_Statistics: \n",
      " Epoch: 67/10000 Training loss: 0.72713 Validation loss: 1.23920 Testing loss: 1.40057 Training Accuracy: 0.76268 Validation Accuracy: 0.58361 Testing AUC: 0.8315\n",
      "Training_Statistics: \n",
      " Epoch: 68/10000 Training loss: 0.71087 Validation loss: 1.23916 Testing loss: 1.40547 Training Accuracy: 0.76186 Validation Accuracy: 0.57705 Testing AUC: 0.8321\n",
      "Done Training\n",
      "Accuracy = 0.580906148867314\n",
      "4\n",
      "Training_Statistics: \n",
      " Epoch: 1/10000 Training loss: 2.25745 Validation loss: 2.15049 Testing loss: 2.15221 Training Accuracy: 0.10089 Validation Accuracy: 0.15738 Testing AUC: 0.52563\n",
      "Training_Statistics: \n",
      " Epoch: 2/10000 Training loss: 2.12052 Validation loss: 2.02859 Testing loss: 2.03349 Training Accuracy: 0.18043 Validation Accuracy: 0.2623 Testing AUC: 0.55512\n",
      "Training_Statistics: \n",
      " Epoch: 3/10000 Training loss: 1.99995 Validation loss: 1.91917 Testing loss: 1.92671 Training Accuracy: 0.33141 Validation Accuracy: 0.3541 Testing AUC: 0.58536\n",
      "Training_Statistics: \n",
      " Epoch: 4/10000 Training loss: 1.89735 Validation loss: 1.84508 Testing loss: 1.85660 Training Accuracy: 0.37661 Validation Accuracy: 0.3541 Testing AUC: 0.61532\n",
      "Training_Statistics: \n",
      " Epoch: 5/10000 Training loss: 1.82893 Validation loss: 1.83769 Testing loss: 1.85408 Training Accuracy: 0.35583 Validation Accuracy: 0.3541 Testing AUC: 0.64382\n",
      "Training_Statistics: \n",
      " Epoch: 6/10000 Training loss: 1.83574 Validation loss: 1.84298 Testing loss: 1.86220 Training Accuracy: 0.35332 Validation Accuracy: 0.3541 Testing AUC: 0.67024\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training_Statistics: \n",
      " Epoch: 7/10000 Training loss: 1.82638 Validation loss: 1.80583 Testing loss: 1.82583 Training Accuracy: 0.35429 Validation Accuracy: 0.3541 Testing AUC: 0.69346\n",
      "Training_Statistics: \n",
      " Epoch: 8/10000 Training loss: 1.78458 Validation loss: 1.77113 Testing loss: 1.79056 Training Accuracy: 0.35494 Validation Accuracy: 0.36066 Testing AUC: 0.71131\n",
      "Training_Statistics: \n",
      " Epoch: 9/10000 Training loss: 1.75257 Validation loss: 1.75359 Testing loss: 1.77300 Training Accuracy: 0.37348 Validation Accuracy: 0.38033 Testing AUC: 0.72197\n",
      "Training_Statistics: \n",
      " Epoch: 10/10000 Training loss: 1.73648 Validation loss: 1.74138 Testing loss: 1.76180 Training Accuracy: 0.3932 Validation Accuracy: 0.39344 Testing AUC: 0.72984\n",
      "Training_Statistics: \n",
      " Epoch: 11/10000 Training loss: 1.71976 Validation loss: 1.72596 Testing loss: 1.74742 Training Accuracy: 0.41735 Validation Accuracy: 0.4 Testing AUC: 0.73839\n",
      "Training_Statistics: \n",
      " Epoch: 12/10000 Training loss: 1.70237 Validation loss: 1.70498 Testing loss: 1.72787 Training Accuracy: 0.42574 Validation Accuracy: 0.40984 Testing AUC: 0.74588\n",
      "Training_Statistics: \n",
      " Epoch: 13/10000 Training loss: 1.67699 Validation loss: 1.68200 Testing loss: 1.70651 Training Accuracy: 0.42574 Validation Accuracy: 0.41311 Testing AUC: 0.75448\n",
      "Training_Statistics: \n",
      " Epoch: 14/10000 Training loss: 1.64894 Validation loss: 1.66047 Testing loss: 1.68661 Training Accuracy: 0.42779 Validation Accuracy: 0.41311 Testing AUC: 0.76055\n",
      "Training_Statistics: \n",
      " Epoch: 15/10000 Training loss: 1.63043 Validation loss: 1.64137 Testing loss: 1.66901 Training Accuracy: 0.42545 Validation Accuracy: 0.42295 Testing AUC: 0.76607\n",
      "Training_Statistics: \n",
      " Epoch: 16/10000 Training loss: 1.60274 Validation loss: 1.62207 Testing loss: 1.65123 Training Accuracy: 0.43321 Validation Accuracy: 0.42951 Testing AUC: 0.77036\n",
      "Training_Statistics: \n",
      " Epoch: 17/10000 Training loss: 1.57876 Validation loss: 1.60056 Testing loss: 1.63082 Training Accuracy: 0.44408 Validation Accuracy: 0.43279 Testing AUC: 0.77321\n",
      "Training_Statistics: \n",
      " Epoch: 18/10000 Training loss: 1.55607 Validation loss: 1.57789 Testing loss: 1.60877 Training Accuracy: 0.45039 Validation Accuracy: 0.43934 Testing AUC: 0.77616\n",
      "Training_Statistics: \n",
      " Epoch: 19/10000 Training loss: 1.52686 Validation loss: 1.55589 Testing loss: 1.58661 Training Accuracy: 0.47141 Validation Accuracy: 0.45574 Testing AUC: 0.77878\n",
      "Training_Statistics: \n",
      " Epoch: 20/10000 Training loss: 1.50175 Validation loss: 1.53460 Testing loss: 1.56496 Training Accuracy: 0.48823 Validation Accuracy: 0.47213 Testing AUC: 0.78203\n",
      "Training_Statistics: \n",
      " Epoch: 21/10000 Training loss: 1.47834 Validation loss: 1.51434 Testing loss: 1.54414 Training Accuracy: 0.49934 Validation Accuracy: 0.47541 Testing AUC: 0.78598\n",
      "Training_Statistics: \n",
      " Epoch: 22/10000 Training loss: 1.45014 Validation loss: 1.49481 Testing loss: 1.52411 Training Accuracy: 0.50566 Validation Accuracy: 0.4918 Testing AUC: 0.79046\n",
      "Training_Statistics: \n",
      " Epoch: 23/10000 Training loss: 1.42445 Validation loss: 1.47612 Testing loss: 1.50523 Training Accuracy: 0.51599 Validation Accuracy: 0.49836 Testing AUC: 0.79394\n",
      "Training_Statistics: \n",
      " Epoch: 24/10000 Training loss: 1.39727 Validation loss: 1.45876 Testing loss: 1.48722 Training Accuracy: 0.52717 Validation Accuracy: 0.49836 Testing AUC: 0.79722\n",
      "Training_Statistics: \n",
      " Epoch: 25/10000 Training loss: 1.37558 Validation loss: 1.44309 Testing loss: 1.47038 Training Accuracy: 0.53099 Validation Accuracy: 0.52787 Testing AUC: 0.80025\n",
      "Training_Statistics: \n",
      " Epoch: 26/10000 Training loss: 1.34816 Validation loss: 1.42869 Testing loss: 1.45317 Training Accuracy: 0.5385 Validation Accuracy: 0.52131 Testing AUC: 0.80479\n",
      "Training_Statistics: \n",
      " Epoch: 27/10000 Training loss: 1.32553 Validation loss: 1.41675 Testing loss: 1.43748 Training Accuracy: 0.53785 Validation Accuracy: 0.52131 Testing AUC: 0.8096\n",
      "Training_Statistics: \n",
      " Epoch: 28/10000 Training loss: 1.30753 Validation loss: 1.40632 Testing loss: 1.42365 Training Accuracy: 0.54821 Validation Accuracy: 0.51803 Testing AUC: 0.81309\n",
      "Training_Statistics: \n",
      " Epoch: 29/10000 Training loss: 1.27878 Validation loss: 1.39778 Testing loss: 1.41202 Training Accuracy: 0.55822 Validation Accuracy: 0.5377 Testing AUC: 0.81481\n",
      "Training_Statistics: \n",
      " Epoch: 30/10000 Training loss: 1.26091 Validation loss: 1.39027 Testing loss: 1.40147 Training Accuracy: 0.55861 Validation Accuracy: 0.53115 Testing AUC: 0.81846\n",
      "Training_Statistics: \n",
      " Epoch: 31/10000 Training loss: 1.24352 Validation loss: 1.38272 Testing loss: 1.39124 Training Accuracy: 0.56111 Validation Accuracy: 0.5377 Testing AUC: 0.82207\n",
      "Training_Statistics: \n",
      " Epoch: 32/10000 Training loss: 1.21897 Validation loss: 1.37561 Testing loss: 1.38122 Training Accuracy: 0.57322 Validation Accuracy: 0.52787 Testing AUC: 0.82489\n",
      "Training_Statistics: \n",
      " Epoch: 33/10000 Training loss: 1.20510 Validation loss: 1.37074 Testing loss: 1.37332 Training Accuracy: 0.58161 Validation Accuracy: 0.52459 Testing AUC: 0.82709\n",
      "Training_Statistics: \n",
      " Epoch: 34/10000 Training loss: 1.18051 Validation loss: 1.36589 Testing loss: 1.36516 Training Accuracy: 0.5973 Validation Accuracy: 0.54426 Testing AUC: 0.82976\n",
      "Training_Statistics: \n",
      " Epoch: 35/10000 Training loss: 1.16529 Validation loss: 1.36288 Testing loss: 1.35885 Training Accuracy: 0.60427 Validation Accuracy: 0.54098 Testing AUC: 0.83193\n",
      "Training_Statistics: \n",
      " Epoch: 36/10000 Training loss: 1.14716 Validation loss: 1.35797 Testing loss: 1.35331 Training Accuracy: 0.61256 Validation Accuracy: 0.55082 Testing AUC: 0.83309\n",
      "Training_Statistics: \n",
      " Epoch: 37/10000 Training loss: 1.13322 Validation loss: 1.35267 Testing loss: 1.34811 Training Accuracy: 0.61856 Validation Accuracy: 0.5541 Testing AUC: 0.83444\n",
      "Training_Statistics: \n",
      " Epoch: 38/10000 Training loss: 1.11592 Validation loss: 1.35086 Testing loss: 1.34605 Training Accuracy: 0.62435 Validation Accuracy: 0.55082 Testing AUC: 0.83702\n",
      "Training_Statistics: \n",
      " Epoch: 39/10000 Training loss: 1.10152 Validation loss: 1.34546 Testing loss: 1.33812 Training Accuracy: 0.62578 Validation Accuracy: 0.54098 Testing AUC: 0.83877\n",
      "Training_Statistics: \n",
      " Epoch: 40/10000 Training loss: 1.08789 Validation loss: 1.33978 Testing loss: 1.33247 Training Accuracy: 0.6341 Validation Accuracy: 0.54098 Testing AUC: 0.84086\n",
      "Training_Statistics: \n",
      " Epoch: 41/10000 Training loss: 1.07266 Validation loss: 1.33308 Testing loss: 1.32789 Training Accuracy: 0.63806 Validation Accuracy: 0.54098 Testing AUC: 0.8421\n",
      "Training_Statistics: \n",
      " Epoch: 42/10000 Training loss: 1.06077 Validation loss: 1.32677 Testing loss: 1.32207 Training Accuracy: 0.64521 Validation Accuracy: 0.5377 Testing AUC: 0.84443\n",
      "Training_Statistics: \n",
      " Epoch: 43/10000 Training loss: 1.04654 Validation loss: 1.32056 Testing loss: 1.31620 Training Accuracy: 0.64159 Validation Accuracy: 0.54426 Testing AUC: 0.84619\n",
      "Training_Statistics: \n",
      " Epoch: 44/10000 Training loss: 1.02669 Validation loss: 1.31714 Testing loss: 1.31228 Training Accuracy: 0.65275 Validation Accuracy: 0.54754 Testing AUC: 0.84723\n",
      "Training_Statistics: \n",
      " Epoch: 45/10000 Training loss: 1.01529 Validation loss: 1.30822 Testing loss: 1.30703 Training Accuracy: 0.659 Validation Accuracy: 0.54426 Testing AUC: 0.84946\n",
      "Training_Statistics: \n",
      " Epoch: 46/10000 Training loss: 1.00426 Validation loss: 1.30137 Testing loss: 1.30164 Training Accuracy: 0.66082 Validation Accuracy: 0.54426 Testing AUC: 0.85128\n",
      "Training_Statistics: \n",
      " Epoch: 47/10000 Training loss: 0.98847 Validation loss: 1.30066 Testing loss: 1.29692 Training Accuracy: 0.66786 Validation Accuracy: 0.5541 Testing AUC: 0.85103\n",
      "Training_Statistics: \n",
      " Epoch: 48/10000 Training loss: 0.97829 Validation loss: 1.29369 Testing loss: 1.29314 Training Accuracy: 0.67025 Validation Accuracy: 0.54754 Testing AUC: 0.85258\n",
      "Training_Statistics: \n",
      " Epoch: 49/10000 Training loss: 0.96306 Validation loss: 1.28907 Testing loss: 1.28920 Training Accuracy: 0.67604 Validation Accuracy: 0.55082 Testing AUC: 0.85528\n",
      "Training_Statistics: \n",
      " Epoch: 50/10000 Training loss: 0.95063 Validation loss: 1.29278 Testing loss: 1.28645 Training Accuracy: 0.67765 Validation Accuracy: 0.5541 Testing AUC: 0.85639\n",
      "Training_Statistics: \n",
      " Epoch: 51/10000 Training loss: 0.94191 Validation loss: 1.28632 Testing loss: 1.28117 Training Accuracy: 0.66997 Validation Accuracy: 0.56393 Testing AUC: 0.85718\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training_Statistics: \n",
      " Epoch: 52/10000 Training loss: 0.92828 Validation loss: 1.28068 Testing loss: 1.28418 Training Accuracy: 0.68851 Validation Accuracy: 0.5541 Testing AUC: 0.85856\n",
      "Training_Statistics: \n",
      " Epoch: 53/10000 Training loss: 0.91381 Validation loss: 1.28659 Testing loss: 1.27971 Training Accuracy: 0.68751 Validation Accuracy: 0.55738 Testing AUC: 0.85987\n",
      "Training_Statistics: \n",
      " Epoch: 54/10000 Training loss: 0.90699 Validation loss: 1.28006 Testing loss: 1.27409 Training Accuracy: 0.68568 Validation Accuracy: 0.57377 Testing AUC: 0.86166\n",
      "Training_Statistics: \n",
      " Epoch: 55/10000 Training loss: 0.88953 Validation loss: 1.28709 Testing loss: 1.27960 Training Accuracy: 0.70494 Validation Accuracy: 0.57049 Testing AUC: 0.86145\n",
      "Training_Statistics: \n",
      " Epoch: 56/10000 Training loss: 0.88062 Validation loss: 1.28079 Testing loss: 1.27437 Training Accuracy: 0.69801 Validation Accuracy: 0.57049 Testing AUC: 0.86207\n",
      "Training_Statistics: \n",
      " Epoch: 57/10000 Training loss: 0.86859 Validation loss: 1.27672 Testing loss: 1.27244 Training Accuracy: 0.70869 Validation Accuracy: 0.57049 Testing AUC: 0.86381\n",
      "Training_Statistics: \n",
      " Epoch: 58/10000 Training loss: 0.86015 Validation loss: 1.28917 Testing loss: 1.27698 Training Accuracy: 0.71141 Validation Accuracy: 0.56393 Testing AUC: 0.86357\n",
      "Training_Statistics: \n",
      " Epoch: 59/10000 Training loss: 0.84714 Validation loss: 1.28011 Testing loss: 1.27241 Training Accuracy: 0.71141 Validation Accuracy: 0.57377 Testing AUC: 0.86442\n",
      "Training_Statistics: \n",
      " Epoch: 60/10000 Training loss: 0.83805 Validation loss: 1.28456 Testing loss: 1.27203 Training Accuracy: 0.71698 Validation Accuracy: 0.57377 Testing AUC: 0.86437\n",
      "Training_Statistics: \n",
      " Epoch: 61/10000 Training loss: 0.82387 Validation loss: 1.28420 Testing loss: 1.27123 Training Accuracy: 0.72284 Validation Accuracy: 0.57049 Testing AUC: 0.86541\n",
      "Training_Statistics: \n",
      " Epoch: 62/10000 Training loss: 0.81495 Validation loss: 1.28119 Testing loss: 1.27027 Training Accuracy: 0.72834 Validation Accuracy: 0.57377 Testing AUC: 0.86714\n",
      "Training_Statistics: \n",
      " Epoch: 63/10000 Training loss: 0.80611 Validation loss: 1.28771 Testing loss: 1.27079 Training Accuracy: 0.73255 Validation Accuracy: 0.56721 Testing AUC: 0.8684\n",
      "Training_Statistics: \n",
      " Epoch: 64/10000 Training loss: 0.79209 Validation loss: 1.29188 Testing loss: 1.27056 Training Accuracy: 0.74013 Validation Accuracy: 0.58033 Testing AUC: 0.86744\n",
      "Training_Statistics: \n",
      " Epoch: 65/10000 Training loss: 0.78490 Validation loss: 1.28950 Testing loss: 1.27471 Training Accuracy: 0.74135 Validation Accuracy: 0.58361 Testing AUC: 0.86828\n",
      "Done Training\n",
      "Accuracy = 0.5812297734627832\n",
      "Monte Carlo Simulation Completed\n"
     ]
    }
   ],
   "source": [
    "DTCR_SS.Monte_Carlo_CrossVal(test_size=0.25,folds=5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Once again, we can view the AUC curve."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "DTCR_SS.AUC_Curve()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To run a K-fold cross validation with 5 folds of the data, fun the following command. In this case, no test_size is required as the algorithm is trained on the entirety of the train folds and tested on the out-fold."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n",
      "Training_Statistics: \n",
      " Epoch: 1/10000 Training loss: 2.17900 Validation loss: 2.11661 Testing loss: 2.10949 Training Accuracy: 0.10933 Validation Accuracy: 0.26899 Testing AUC: 0.46933\n",
      "Training_Statistics: \n",
      " Epoch: 2/10000 Training loss: 2.08444 Validation loss: 2.03432 Testing loss: 2.02139 Training Accuracy: 0.31417 Validation Accuracy: 0.34292 Testing AUC: 0.49514\n",
      "Training_Statistics: \n",
      " Epoch: 3/10000 Training loss: 1.99124 Validation loss: 1.96070 Testing loss: 1.94110 Training Accuracy: 0.35701 Validation Accuracy: 0.33881 Testing AUC: 0.5179\n",
      "Training_Statistics: \n",
      " Epoch: 4/10000 Training loss: 1.90752 Validation loss: 1.90229 Testing loss: 1.87517 Training Accuracy: 0.35451 Validation Accuracy: 0.34086 Testing AUC: 0.53799\n",
      "Training_Statistics: \n",
      " Epoch: 5/10000 Training loss: 1.85615 Validation loss: 1.87383 Testing loss: 1.83917 Training Accuracy: 0.34871 Validation Accuracy: 0.34292 Testing AUC: 0.55597\n",
      "Training_Statistics: \n",
      " Epoch: 6/10000 Training loss: 1.83424 Validation loss: 1.87040 Testing loss: 1.83207 Training Accuracy: 0.34631 Validation Accuracy: 0.34292 Testing AUC: 0.57767\n",
      "Training_Statistics: \n",
      " Epoch: 7/10000 Training loss: 1.80315 Validation loss: 1.85502 Testing loss: 1.82007 Training Accuracy: 0.36022 Validation Accuracy: 0.34292 Testing AUC: 0.59976\n",
      "Training_Statistics: \n",
      " Epoch: 8/10000 Training loss: 1.75602 Validation loss: 1.82777 Testing loss: 1.79907 Training Accuracy: 0.36486 Validation Accuracy: 0.34292 Testing AUC: 0.62111\n",
      "Training_Statistics: \n",
      " Epoch: 9/10000 Training loss: 1.74706 Validation loss: 1.80109 Testing loss: 1.77883 Training Accuracy: 0.35701 Validation Accuracy: 0.36345 Testing AUC: 0.64263\n",
      "Training_Statistics: \n",
      " Epoch: 10/10000 Training loss: 1.72987 Validation loss: 1.77757 Testing loss: 1.76131 Training Accuracy: 0.39228 Validation Accuracy: 0.3922 Testing AUC: 0.6613\n",
      "Training_Statistics: \n",
      " Epoch: 11/10000 Training loss: 1.68483 Validation loss: 1.75639 Testing loss: 1.74442 Training Accuracy: 0.4258 Validation Accuracy: 0.39014 Testing AUC: 0.67825\n",
      "Training_Statistics: \n",
      " Epoch: 12/10000 Training loss: 1.67891 Validation loss: 1.73545 Testing loss: 1.72544 Training Accuracy: 0.41338 Validation Accuracy: 0.3922 Testing AUC: 0.69216\n",
      "Training_Statistics: \n",
      " Epoch: 13/10000 Training loss: 1.64851 Validation loss: 1.71394 Testing loss: 1.70430 Training Accuracy: 0.42318 Validation Accuracy: 0.39836 Testing AUC: 0.70309\n",
      "Training_Statistics: \n",
      " Epoch: 14/10000 Training loss: 1.62598 Validation loss: 1.69211 Testing loss: 1.68136 Training Accuracy: 0.43192 Validation Accuracy: 0.39836 Testing AUC: 0.71143\n",
      "Training_Statistics: \n",
      " Epoch: 15/10000 Training loss: 1.59010 Validation loss: 1.67072 Testing loss: 1.65831 Training Accuracy: 0.44554 Validation Accuracy: 0.40862 Testing AUC: 0.71838\n",
      "Training_Statistics: \n",
      " Epoch: 16/10000 Training loss: 1.55280 Validation loss: 1.64891 Testing loss: 1.63647 Training Accuracy: 0.45194 Validation Accuracy: 0.41068 Testing AUC: 0.72489\n",
      "Training_Statistics: \n",
      " Epoch: 17/10000 Training loss: 1.53015 Validation loss: 1.62542 Testing loss: 1.61571 Training Accuracy: 0.45454 Validation Accuracy: 0.41478 Testing AUC: 0.73278\n",
      "Training_Statistics: \n",
      " Epoch: 18/10000 Training loss: 1.50027 Validation loss: 1.60127 Testing loss: 1.59656 Training Accuracy: 0.46354 Validation Accuracy: 0.43737 Testing AUC: 0.74113\n",
      "Training_Statistics: \n",
      " Epoch: 19/10000 Training loss: 1.46727 Validation loss: 1.57805 Testing loss: 1.57928 Training Accuracy: 0.5031 Validation Accuracy: 0.46817 Testing AUC: 0.74987\n",
      "Training_Statistics: \n",
      " Epoch: 20/10000 Training loss: 1.42567 Validation loss: 1.55689 Testing loss: 1.56163 Training Accuracy: 0.50978 Validation Accuracy: 0.47639 Testing AUC: 0.75766\n",
      "Training_Statistics: \n",
      " Epoch: 21/10000 Training loss: 1.39776 Validation loss: 1.53794 Testing loss: 1.54343 Training Accuracy: 0.51834 Validation Accuracy: 0.47228 Testing AUC: 0.76468\n",
      "Training_Statistics: \n",
      " Epoch: 22/10000 Training loss: 1.37771 Validation loss: 1.52185 Testing loss: 1.52707 Training Accuracy: 0.523 Validation Accuracy: 0.47228 Testing AUC: 0.77146\n",
      "Training_Statistics: \n",
      " Epoch: 23/10000 Training loss: 1.35391 Validation loss: 1.50688 Testing loss: 1.51129 Training Accuracy: 0.52732 Validation Accuracy: 0.47023 Testing AUC: 0.77716\n",
      "Training_Statistics: \n",
      " Epoch: 24/10000 Training loss: 1.32012 Validation loss: 1.49181 Testing loss: 1.49493 Training Accuracy: 0.5343 Validation Accuracy: 0.48255 Testing AUC: 0.78308\n",
      "Training_Statistics: \n",
      " Epoch: 25/10000 Training loss: 1.30453 Validation loss: 1.47775 Testing loss: 1.47975 Training Accuracy: 0.5359 Validation Accuracy: 0.48871 Testing AUC: 0.78841\n",
      "Training_Statistics: \n",
      " Epoch: 26/10000 Training loss: 1.29024 Validation loss: 1.46304 Testing loss: 1.46782 Training Accuracy: 0.54156 Validation Accuracy: 0.50103 Testing AUC: 0.79372\n",
      "Training_Statistics: \n",
      " Epoch: 27/10000 Training loss: 1.26686 Validation loss: 1.45181 Testing loss: 1.45730 Training Accuracy: 0.55386 Validation Accuracy: 0.49692 Testing AUC: 0.79781\n",
      "Training_Statistics: \n",
      " Epoch: 28/10000 Training loss: 1.25901 Validation loss: 1.44330 Testing loss: 1.44149 Training Accuracy: 0.55338 Validation Accuracy: 0.50719 Testing AUC: 0.80068\n",
      "Training_Statistics: \n",
      " Epoch: 29/10000 Training loss: 1.23576 Validation loss: 1.43921 Testing loss: 1.42792 Training Accuracy: 0.55148 Validation Accuracy: 0.51335 Testing AUC: 0.80281\n",
      "Training_Statistics: \n",
      " Epoch: 30/10000 Training loss: 1.21940 Validation loss: 1.42300 Testing loss: 1.41640 Training Accuracy: 0.56312 Validation Accuracy: 0.51745 Testing AUC: 0.80774\n",
      "Training_Statistics: \n",
      " Epoch: 31/10000 Training loss: 1.19068 Validation loss: 1.41429 Testing loss: 1.41076 Training Accuracy: 0.58116 Validation Accuracy: 0.49897 Testing AUC: 0.81194\n",
      "Training_Statistics: \n",
      " Epoch: 32/10000 Training loss: 1.17820 Validation loss: 1.40776 Testing loss: 1.39322 Training Accuracy: 0.59346 Validation Accuracy: 0.51745 Testing AUC: 0.813\n",
      "Training_Statistics: \n",
      " Epoch: 33/10000 Training loss: 1.14445 Validation loss: 1.40408 Testing loss: 1.38228 Training Accuracy: 0.60741 Validation Accuracy: 0.53388 Testing AUC: 0.81454\n",
      "Training_Statistics: \n",
      " Epoch: 34/10000 Training loss: 1.13512 Validation loss: 1.39472 Testing loss: 1.37716 Training Accuracy: 0.60194 Validation Accuracy: 0.53388 Testing AUC: 0.81848\n",
      "Training_Statistics: \n",
      " Epoch: 35/10000 Training loss: 1.12374 Validation loss: 1.38535 Testing loss: 1.36744 Training Accuracy: 0.60354 Validation Accuracy: 0.52977 Testing AUC: 0.82167\n",
      "Training_Statistics: \n",
      " Epoch: 36/10000 Training loss: 1.12786 Validation loss: 1.38355 Testing loss: 1.35933 Training Accuracy: 0.6039 Validation Accuracy: 0.52567 Testing AUC: 0.82286\n",
      "Training_Statistics: \n",
      " Epoch: 37/10000 Training loss: 1.11617 Validation loss: 1.37819 Testing loss: 1.35275 Training Accuracy: 0.6114 Validation Accuracy: 0.52156 Testing AUC: 0.8247\n",
      "Training_Statistics: \n",
      " Epoch: 38/10000 Training loss: 1.08599 Validation loss: 1.37174 Testing loss: 1.34872 Training Accuracy: 0.62352 Validation Accuracy: 0.52567 Testing AUC: 0.8274\n",
      "Training_Statistics: \n",
      " Epoch: 39/10000 Training loss: 1.07957 Validation loss: 1.37002 Testing loss: 1.34447 Training Accuracy: 0.62504 Validation Accuracy: 0.54004 Testing AUC: 0.82789\n",
      "Training_Statistics: \n",
      " Epoch: 40/10000 Training loss: 1.05522 Validation loss: 1.37325 Testing loss: 1.33820 Training Accuracy: 0.63583 Validation Accuracy: 0.52567 Testing AUC: 0.82733\n",
      "Training_Statistics: \n",
      " Epoch: 41/10000 Training loss: 1.03535 Validation loss: 1.37101 Testing loss: 1.33527 Training Accuracy: 0.63865 Validation Accuracy: 0.52567 Testing AUC: 0.82895\n",
      "Training_Statistics: \n",
      " Epoch: 42/10000 Training loss: 1.02287 Validation loss: 1.36354 Testing loss: 1.33327 Training Accuracy: 0.65477 Validation Accuracy: 0.53183 Testing AUC: 0.83065\n",
      "Training_Statistics: \n",
      " Epoch: 43/10000 Training loss: 1.01257 Validation loss: 1.36337 Testing loss: 1.33285 Training Accuracy: 0.64981 Validation Accuracy: 0.54825 Testing AUC: 0.83063\n",
      "Training_Statistics: \n",
      " Epoch: 44/10000 Training loss: 0.99017 Validation loss: 1.36632 Testing loss: 1.33315 Training Accuracy: 0.65569 Validation Accuracy: 0.55031 Testing AUC: 0.83027\n",
      "Training_Statistics: \n",
      " Epoch: 45/10000 Training loss: 0.98946 Validation loss: 1.36666 Testing loss: 1.33263 Training Accuracy: 0.66393 Validation Accuracy: 0.52977 Testing AUC: 0.8305\n",
      "Training_Statistics: \n",
      " Epoch: 46/10000 Training loss: 0.99023 Validation loss: 1.36727 Testing loss: 1.32549 Training Accuracy: 0.65557 Validation Accuracy: 0.54004 Testing AUC: 0.83142\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training_Statistics: \n",
      " Epoch: 47/10000 Training loss: 0.96717 Validation loss: 1.36468 Testing loss: 1.32359 Training Accuracy: 0.66821 Validation Accuracy: 0.55236 Testing AUC: 0.83189\n",
      "Training_Statistics: \n",
      " Epoch: 48/10000 Training loss: 0.93214 Validation loss: 1.36310 Testing loss: 1.33099 Training Accuracy: 0.67857 Validation Accuracy: 0.53799 Testing AUC: 0.83243\n",
      "Training_Statistics: \n",
      " Epoch: 49/10000 Training loss: 0.92472 Validation loss: 1.37199 Testing loss: 1.32486 Training Accuracy: 0.66795 Validation Accuracy: 0.55441 Testing AUC: 0.83136\n",
      "Training_Statistics: \n",
      " Epoch: 50/10000 Training loss: 0.91818 Validation loss: 1.37324 Testing loss: 1.32258 Training Accuracy: 0.68323 Validation Accuracy: 0.53799 Testing AUC: 0.83212\n",
      "Training_Statistics: \n",
      " Epoch: 51/10000 Training loss: 0.89758 Validation loss: 1.36574 Testing loss: 1.32715 Training Accuracy: 0.68689 Validation Accuracy: 0.54415 Testing AUC: 0.83461\n",
      "Done Training\n",
      "Accuracy = 0.5215605749486653\n",
      "1\n",
      "Training_Statistics: \n",
      " Epoch: 1/10000 Training loss: 2.17333 Validation loss: 2.06740 Testing loss: 2.07286 Training Accuracy: 0.14009 Validation Accuracy: 0.25257 Testing AUC: 0.54752\n",
      "Training_Statistics: \n",
      " Epoch: 2/10000 Training loss: 2.05219 Validation loss: 1.94771 Testing loss: 1.96091 Training Accuracy: 0.28851 Validation Accuracy: 0.3614 Testing AUC: 0.57764\n",
      "Training_Statistics: \n",
      " Epoch: 3/10000 Training loss: 1.92508 Validation loss: 1.87281 Testing loss: 1.89624 Training Accuracy: 0.35543 Validation Accuracy: 0.3614 Testing AUC: 0.60629\n",
      "Training_Statistics: \n",
      " Epoch: 4/10000 Training loss: 1.87027 Validation loss: 1.85784 Testing loss: 1.88761 Training Accuracy: 0.36006 Validation Accuracy: 0.3614 Testing AUC: 0.63747\n",
      "Training_Statistics: \n",
      " Epoch: 5/10000 Training loss: 1.86823 Validation loss: 1.82405 Testing loss: 1.85243 Training Accuracy: 0.34963 Validation Accuracy: 0.3614 Testing AUC: 0.67416\n",
      "Training_Statistics: \n",
      " Epoch: 6/10000 Training loss: 1.82429 Validation loss: 1.79253 Testing loss: 1.81765 Training Accuracy: 0.35543 Validation Accuracy: 0.36345 Testing AUC: 0.70469\n",
      "Training_Statistics: \n",
      " Epoch: 7/10000 Training loss: 1.79103 Validation loss: 1.77087 Testing loss: 1.79406 Training Accuracy: 0.35269 Validation Accuracy: 0.38193 Testing AUC: 0.72445\n",
      "Training_Statistics: \n",
      " Epoch: 8/10000 Training loss: 1.77306 Validation loss: 1.74905 Testing loss: 1.77166 Training Accuracy: 0.38478 Validation Accuracy: 0.423 Testing AUC: 0.73698\n",
      "Training_Statistics: \n",
      " Epoch: 9/10000 Training loss: 1.76090 Validation loss: 1.72586 Testing loss: 1.74816 Training Accuracy: 0.40172 Validation Accuracy: 0.42505 Testing AUC: 0.74656\n",
      "Training_Statistics: \n",
      " Epoch: 10/10000 Training loss: 1.74311 Validation loss: 1.70260 Testing loss: 1.72413 Training Accuracy: 0.40033 Validation Accuracy: 0.42505 Testing AUC: 0.75459\n",
      "Training_Statistics: \n",
      " Epoch: 11/10000 Training loss: 1.69070 Validation loss: 1.68026 Testing loss: 1.69991 Training Accuracy: 0.41938 Validation Accuracy: 0.4271 Testing AUC: 0.76227\n",
      "Training_Statistics: \n",
      " Epoch: 12/10000 Training loss: 1.69146 Validation loss: 1.65857 Testing loss: 1.67533 Training Accuracy: 0.40836 Validation Accuracy: 0.4271 Testing AUC: 0.76669\n",
      "Training_Statistics: \n",
      " Epoch: 13/10000 Training loss: 1.65507 Validation loss: 1.63664 Testing loss: 1.64946 Training Accuracy: 0.4102 Validation Accuracy: 0.43737 Testing AUC: 0.77099\n",
      "Training_Statistics: \n",
      " Epoch: 14/10000 Training loss: 1.61971 Validation loss: 1.61451 Testing loss: 1.62234 Training Accuracy: 0.42988 Validation Accuracy: 0.44353 Testing AUC: 0.77541\n",
      "Training_Statistics: \n",
      " Epoch: 15/10000 Training loss: 1.57493 Validation loss: 1.59326 Testing loss: 1.59532 Training Accuracy: 0.44202 Validation Accuracy: 0.44764 Testing AUC: 0.77966\n",
      "Training_Statistics: \n",
      " Epoch: 16/10000 Training loss: 1.56207 Validation loss: 1.57188 Testing loss: 1.56870 Training Accuracy: 0.44552 Validation Accuracy: 0.43737 Testing AUC: 0.78306\n",
      "Training_Statistics: \n",
      " Epoch: 17/10000 Training loss: 1.53907 Validation loss: 1.55079 Testing loss: 1.54296 Training Accuracy: 0.44556 Validation Accuracy: 0.43943 Testing AUC: 0.78658\n",
      "Training_Statistics: \n",
      " Epoch: 18/10000 Training loss: 1.51308 Validation loss: 1.53118 Testing loss: 1.51718 Training Accuracy: 0.45048 Validation Accuracy: 0.44969 Testing AUC: 0.78992\n",
      "Training_Statistics: \n",
      " Epoch: 19/10000 Training loss: 1.45995 Validation loss: 1.51362 Testing loss: 1.49174 Training Accuracy: 0.48718 Validation Accuracy: 0.45585 Testing AUC: 0.79307\n",
      "Training_Statistics: \n",
      " Epoch: 20/10000 Training loss: 1.47042 Validation loss: 1.49846 Testing loss: 1.46754 Training Accuracy: 0.48958 Validation Accuracy: 0.45996 Testing AUC: 0.79607\n",
      "Training_Statistics: \n",
      " Epoch: 21/10000 Training loss: 1.42056 Validation loss: 1.48594 Testing loss: 1.44462 Training Accuracy: 0.51076 Validation Accuracy: 0.46407 Testing AUC: 0.7994\n",
      "Training_Statistics: \n",
      " Epoch: 22/10000 Training loss: 1.40640 Validation loss: 1.47476 Testing loss: 1.42296 Training Accuracy: 0.50646 Validation Accuracy: 0.46612 Testing AUC: 0.80248\n",
      "Training_Statistics: \n",
      " Epoch: 23/10000 Training loss: 1.39279 Validation loss: 1.46547 Testing loss: 1.40262 Training Accuracy: 0.50198 Validation Accuracy: 0.46817 Testing AUC: 0.80578\n",
      "Training_Statistics: \n",
      " Epoch: 24/10000 Training loss: 1.34811 Validation loss: 1.45622 Testing loss: 1.38418 Training Accuracy: 0.521 Validation Accuracy: 0.4846 Testing AUC: 0.80895\n",
      "Training_Statistics: \n",
      " Epoch: 25/10000 Training loss: 1.34782 Validation loss: 1.44787 Testing loss: 1.36785 Training Accuracy: 0.52088 Validation Accuracy: 0.49076 Testing AUC: 0.81066\n",
      "Training_Statistics: \n",
      " Epoch: 26/10000 Training loss: 1.29933 Validation loss: 1.44104 Testing loss: 1.35247 Training Accuracy: 0.52874 Validation Accuracy: 0.49076 Testing AUC: 0.81243\n",
      "Training_Statistics: \n",
      " Epoch: 27/10000 Training loss: 1.27185 Validation loss: 1.43533 Testing loss: 1.33882 Training Accuracy: 0.54312 Validation Accuracy: 0.49487 Testing AUC: 0.81463\n",
      "Training_Statistics: \n",
      " Epoch: 28/10000 Training loss: 1.26849 Validation loss: 1.42669 Testing loss: 1.32813 Training Accuracy: 0.53768 Validation Accuracy: 0.49281 Testing AUC: 0.81626\n",
      "Training_Statistics: \n",
      " Epoch: 29/10000 Training loss: 1.23679 Validation loss: 1.42105 Testing loss: 1.31789 Training Accuracy: 0.54896 Validation Accuracy: 0.49281 Testing AUC: 0.81822\n",
      "Training_Statistics: \n",
      " Epoch: 30/10000 Training loss: 1.23030 Validation loss: 1.41665 Testing loss: 1.30861 Training Accuracy: 0.55474 Validation Accuracy: 0.50719 Testing AUC: 0.81963\n",
      "Training_Statistics: \n",
      " Epoch: 31/10000 Training loss: 1.20807 Validation loss: 1.41495 Testing loss: 1.30238 Training Accuracy: 0.57468 Validation Accuracy: 0.50719 Testing AUC: 0.82122\n",
      "Training_Statistics: \n",
      " Epoch: 32/10000 Training loss: 1.18718 Validation loss: 1.40551 Testing loss: 1.29558 Training Accuracy: 0.57594 Validation Accuracy: 0.50513 Testing AUC: 0.82103\n",
      "Training_Statistics: \n",
      " Epoch: 33/10000 Training loss: 1.16972 Validation loss: 1.39831 Testing loss: 1.29170 Training Accuracy: 0.57794 Validation Accuracy: 0.50924 Testing AUC: 0.82149\n",
      "Training_Statistics: \n",
      " Epoch: 34/10000 Training loss: 1.15485 Validation loss: 1.39656 Testing loss: 1.28349 Training Accuracy: 0.59722 Validation Accuracy: 0.51129 Testing AUC: 0.82377\n",
      "Training_Statistics: \n",
      " Epoch: 35/10000 Training loss: 1.13861 Validation loss: 1.39532 Testing loss: 1.27579 Training Accuracy: 0.5965 Validation Accuracy: 0.51335 Testing AUC: 0.8249\n",
      "Training_Statistics: \n",
      " Epoch: 36/10000 Training loss: 1.11155 Validation loss: 1.38696 Testing loss: 1.27024 Training Accuracy: 0.62149 Validation Accuracy: 0.51745 Testing AUC: 0.82498\n",
      "Training_Statistics: \n",
      " Epoch: 37/10000 Training loss: 1.09682 Validation loss: 1.38512 Testing loss: 1.26224 Training Accuracy: 0.62217 Validation Accuracy: 0.51745 Testing AUC: 0.82579\n",
      "Training_Statistics: \n",
      " Epoch: 38/10000 Training loss: 1.08707 Validation loss: 1.38632 Testing loss: 1.25702 Training Accuracy: 0.63157 Validation Accuracy: 0.52361 Testing AUC: 0.82665\n",
      "Training_Statistics: \n",
      " Epoch: 39/10000 Training loss: 1.05329 Validation loss: 1.38422 Testing loss: 1.25755 Training Accuracy: 0.63547 Validation Accuracy: 0.51335 Testing AUC: 0.82784\n",
      "Training_Statistics: \n",
      " Epoch: 40/10000 Training loss: 1.05520 Validation loss: 1.38289 Testing loss: 1.25497 Training Accuracy: 0.63252 Validation Accuracy: 0.52156 Testing AUC: 0.82799\n",
      "Training_Statistics: \n",
      " Epoch: 41/10000 Training loss: 1.05224 Validation loss: 1.37717 Testing loss: 1.24807 Training Accuracy: 0.6351 Validation Accuracy: 0.52361 Testing AUC: 0.82814\n",
      "Training_Statistics: \n",
      " Epoch: 42/10000 Training loss: 1.00869 Validation loss: 1.37614 Testing loss: 1.24539 Training Accuracy: 0.64971 Validation Accuracy: 0.52567 Testing AUC: 0.82858\n",
      "Training_Statistics: \n",
      " Epoch: 43/10000 Training loss: 0.99750 Validation loss: 1.38177 Testing loss: 1.24750 Training Accuracy: 0.65685 Validation Accuracy: 0.52772 Testing AUC: 0.82817\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training_Statistics: \n",
      " Epoch: 44/10000 Training loss: 0.99729 Validation loss: 1.37765 Testing loss: 1.24509 Training Accuracy: 0.65827 Validation Accuracy: 0.52977 Testing AUC: 0.82907\n",
      "Training_Statistics: \n",
      " Epoch: 45/10000 Training loss: 0.98363 Validation loss: 1.37739 Testing loss: 1.24299 Training Accuracy: 0.65531 Validation Accuracy: 0.53593 Testing AUC: 0.82982\n",
      "Training_Statistics: \n",
      " Epoch: 46/10000 Training loss: 0.98145 Validation loss: 1.37276 Testing loss: 1.24268 Training Accuracy: 0.66129 Validation Accuracy: 0.54004 Testing AUC: 0.82951\n",
      "Training_Statistics: \n",
      " Epoch: 47/10000 Training loss: 0.94712 Validation loss: 1.37948 Testing loss: 1.25008 Training Accuracy: 0.67649 Validation Accuracy: 0.52567 Testing AUC: 0.82817\n",
      "Training_Statistics: \n",
      " Epoch: 48/10000 Training loss: 0.94200 Validation loss: 1.38699 Testing loss: 1.24391 Training Accuracy: 0.67227 Validation Accuracy: 0.54004 Testing AUC: 0.82817\n",
      "Training_Statistics: \n",
      " Epoch: 49/10000 Training loss: 0.93907 Validation loss: 1.38770 Testing loss: 1.24573 Training Accuracy: 0.67515 Validation Accuracy: 0.54004 Testing AUC: 0.82883\n",
      "Training_Statistics: \n",
      " Epoch: 50/10000 Training loss: 0.91077 Validation loss: 1.37419 Testing loss: 1.25279 Training Accuracy: 0.68601 Validation Accuracy: 0.52567 Testing AUC: 0.83052\n",
      "Training_Statistics: \n",
      " Epoch: 51/10000 Training loss: 0.90361 Validation loss: 1.37552 Testing loss: 1.25006 Training Accuracy: 0.68485 Validation Accuracy: 0.53388 Testing AUC: 0.8296\n",
      "Training_Statistics: \n",
      " Epoch: 52/10000 Training loss: 0.88691 Validation loss: 1.38416 Testing loss: 1.24823 Training Accuracy: 0.70987 Validation Accuracy: 0.53388 Testing AUC: 0.8287\n",
      "Training_Statistics: \n",
      " Epoch: 53/10000 Training loss: 0.87297 Validation loss: 1.37572 Testing loss: 1.25012 Training Accuracy: 0.70881 Validation Accuracy: 0.52156 Testing AUC: 0.82969\n",
      "Training_Statistics: \n",
      " Epoch: 54/10000 Training loss: 0.83995 Validation loss: 1.38008 Testing loss: 1.25285 Training Accuracy: 0.71917 Validation Accuracy: 0.53388 Testing AUC: 0.82931\n",
      "Training_Statistics: \n",
      " Epoch: 55/10000 Training loss: 0.84560 Validation loss: 1.39722 Testing loss: 1.25540 Training Accuracy: 0.71265 Validation Accuracy: 0.52567 Testing AUC: 0.82851\n",
      "Training_Statistics: \n",
      " Epoch: 56/10000 Training loss: 0.82738 Validation loss: 1.39521 Testing loss: 1.26089 Training Accuracy: 0.74437 Validation Accuracy: 0.54209 Testing AUC: 0.83013\n",
      "Done Training\n",
      "Accuracy = 0.5626283367556468\n",
      "2\n",
      "Training_Statistics: \n",
      " Epoch: 1/10000 Training loss: 2.19870 Validation loss: 2.10794 Testing loss: 2.10600 Training Accuracy: 0.12507 Validation Accuracy: 0.23614 Testing AUC: 0.50903\n",
      "Training_Statistics: \n",
      " Epoch: 2/10000 Training loss: 2.08323 Validation loss: 2.00109 Testing loss: 2.00309 Training Accuracy: 0.27769 Validation Accuracy: 0.30801 Testing AUC: 0.53702\n",
      "Training_Statistics: \n",
      " Epoch: 3/10000 Training loss: 1.98677 Validation loss: 1.91270 Testing loss: 1.91937 Training Accuracy: 0.32377 Validation Accuracy: 0.35934 Testing AUC: 0.55764\n",
      "Training_Statistics: \n",
      " Epoch: 4/10000 Training loss: 1.88819 Validation loss: 1.84994 Testing loss: 1.86150 Training Accuracy: 0.36438 Validation Accuracy: 0.35729 Testing AUC: 0.57333\n",
      "Training_Statistics: \n",
      " Epoch: 5/10000 Training loss: 1.85856 Validation loss: 1.82362 Testing loss: 1.83881 Training Accuracy: 0.34915 Validation Accuracy: 0.35729 Testing AUC: 0.59045\n",
      "Training_Statistics: \n",
      " Epoch: 6/10000 Training loss: 1.83324 Validation loss: 1.81035 Testing loss: 1.82521 Training Accuracy: 0.35263 Validation Accuracy: 0.35729 Testing AUC: 0.61378\n",
      "Training_Statistics: \n",
      " Epoch: 7/10000 Training loss: 1.81075 Validation loss: 1.78358 Testing loss: 1.79442 Training Accuracy: 0.35611 Validation Accuracy: 0.35729 Testing AUC: 0.63913\n",
      "Training_Statistics: \n",
      " Epoch: 8/10000 Training loss: 1.77857 Validation loss: 1.75617 Testing loss: 1.76172 Training Accuracy: 0.35653 Validation Accuracy: 0.35934 Testing AUC: 0.66338\n",
      "Training_Statistics: \n",
      " Epoch: 9/10000 Training loss: 1.74179 Validation loss: 1.73671 Testing loss: 1.73850 Training Accuracy: 0.37622 Validation Accuracy: 0.39425 Testing AUC: 0.68625\n",
      "Training_Statistics: \n",
      " Epoch: 10/10000 Training loss: 1.71759 Validation loss: 1.72094 Testing loss: 1.72068 Training Accuracy: 0.41756 Validation Accuracy: 0.41068 Testing AUC: 0.70411\n",
      "Training_Statistics: \n",
      " Epoch: 11/10000 Training loss: 1.70219 Validation loss: 1.70185 Testing loss: 1.70138 Training Accuracy: 0.42316 Validation Accuracy: 0.42094 Testing AUC: 0.71821\n",
      "Training_Statistics: \n",
      " Epoch: 12/10000 Training loss: 1.68549 Validation loss: 1.67757 Testing loss: 1.67874 Training Accuracy: 0.42982 Validation Accuracy: 0.41684 Testing AUC: 0.73022\n",
      "Training_Statistics: \n",
      " Epoch: 13/10000 Training loss: 1.64769 Validation loss: 1.65216 Testing loss: 1.65582 Training Accuracy: 0.43272 Validation Accuracy: 0.41889 Testing AUC: 0.74095\n",
      "Training_Statistics: \n",
      " Epoch: 14/10000 Training loss: 1.62665 Validation loss: 1.62809 Testing loss: 1.63472 Training Accuracy: 0.43024 Validation Accuracy: 0.41684 Testing AUC: 0.75003\n",
      "Training_Statistics: \n",
      " Epoch: 15/10000 Training loss: 1.58367 Validation loss: 1.60460 Testing loss: 1.61324 Training Accuracy: 0.43844 Validation Accuracy: 0.41684 Testing AUC: 0.7582\n",
      "Training_Statistics: \n",
      " Epoch: 16/10000 Training loss: 1.57408 Validation loss: 1.57918 Testing loss: 1.58857 Training Accuracy: 0.43424 Validation Accuracy: 0.41273 Testing AUC: 0.76662\n",
      "Training_Statistics: \n",
      " Epoch: 17/10000 Training loss: 1.52485 Validation loss: 1.55259 Testing loss: 1.56103 Training Accuracy: 0.46088 Validation Accuracy: 0.44353 Testing AUC: 0.7758\n",
      "Training_Statistics: \n",
      " Epoch: 18/10000 Training loss: 1.51390 Validation loss: 1.52795 Testing loss: 1.53457 Training Accuracy: 0.46916 Validation Accuracy: 0.47433 Testing AUC: 0.78439\n",
      "Training_Statistics: \n",
      " Epoch: 19/10000 Training loss: 1.49703 Validation loss: 1.50481 Testing loss: 1.51008 Training Accuracy: 0.48168 Validation Accuracy: 0.47433 Testing AUC: 0.79175\n",
      "Training_Statistics: \n",
      " Epoch: 20/10000 Training loss: 1.45279 Validation loss: 1.48238 Testing loss: 1.48692 Training Accuracy: 0.51492 Validation Accuracy: 0.49076 Testing AUC: 0.79748\n",
      "Training_Statistics: \n",
      " Epoch: 21/10000 Training loss: 1.42942 Validation loss: 1.46054 Testing loss: 1.46496 Training Accuracy: 0.51352 Validation Accuracy: 0.50308 Testing AUC: 0.80144\n",
      "Training_Statistics: \n",
      " Epoch: 22/10000 Training loss: 1.39478 Validation loss: 1.44082 Testing loss: 1.44494 Training Accuracy: 0.52208 Validation Accuracy: 0.49897 Testing AUC: 0.80487\n",
      "Training_Statistics: \n",
      " Epoch: 23/10000 Training loss: 1.37914 Validation loss: 1.42236 Testing loss: 1.42612 Training Accuracy: 0.51796 Validation Accuracy: 0.51129 Testing AUC: 0.80795\n",
      "Training_Statistics: \n",
      " Epoch: 24/10000 Training loss: 1.32998 Validation loss: 1.40509 Testing loss: 1.40867 Training Accuracy: 0.5353 Validation Accuracy: 0.51129 Testing AUC: 0.81185\n",
      "Training_Statistics: \n",
      " Epoch: 25/10000 Training loss: 1.34183 Validation loss: 1.39109 Testing loss: 1.39516 Training Accuracy: 0.52482 Validation Accuracy: 0.52156 Testing AUC: 0.81518\n",
      "Training_Statistics: \n",
      " Epoch: 26/10000 Training loss: 1.29917 Validation loss: 1.37781 Testing loss: 1.38107 Training Accuracy: 0.54662 Validation Accuracy: 0.52361 Testing AUC: 0.81789\n",
      "Training_Statistics: \n",
      " Epoch: 27/10000 Training loss: 1.27832 Validation loss: 1.36409 Testing loss: 1.36681 Training Accuracy: 0.55452 Validation Accuracy: 0.52772 Testing AUC: 0.82058\n",
      "Training_Statistics: \n",
      " Epoch: 28/10000 Training loss: 1.24251 Validation loss: 1.34937 Testing loss: 1.35386 Training Accuracy: 0.55892 Validation Accuracy: 0.53799 Testing AUC: 0.82205\n",
      "Training_Statistics: \n",
      " Epoch: 29/10000 Training loss: 1.23307 Validation loss: 1.33483 Testing loss: 1.34293 Training Accuracy: 0.56508 Validation Accuracy: 0.53799 Testing AUC: 0.82386\n",
      "Training_Statistics: \n",
      " Epoch: 30/10000 Training loss: 1.20976 Validation loss: 1.32593 Testing loss: 1.33586 Training Accuracy: 0.56752 Validation Accuracy: 0.54209 Testing AUC: 0.82562\n",
      "Training_Statistics: \n",
      " Epoch: 31/10000 Training loss: 1.18377 Validation loss: 1.31723 Testing loss: 1.32984 Training Accuracy: 0.5912 Validation Accuracy: 0.54825 Testing AUC: 0.82674\n",
      "Training_Statistics: \n",
      " Epoch: 32/10000 Training loss: 1.17106 Validation loss: 1.31110 Testing loss: 1.32394 Training Accuracy: 0.59738 Validation Accuracy: 0.55441 Testing AUC: 0.82816\n",
      "Training_Statistics: \n",
      " Epoch: 33/10000 Training loss: 1.15701 Validation loss: 1.30353 Testing loss: 1.31286 Training Accuracy: 0.60146 Validation Accuracy: 0.56879 Testing AUC: 0.83003\n",
      "Training_Statistics: \n",
      " Epoch: 34/10000 Training loss: 1.14097 Validation loss: 1.29774 Testing loss: 1.30381 Training Accuracy: 0.61786 Validation Accuracy: 0.5729 Testing AUC: 0.83279\n",
      "Training_Statistics: \n",
      " Epoch: 35/10000 Training loss: 1.12445 Validation loss: 1.28998 Testing loss: 1.29789 Training Accuracy: 0.62444 Validation Accuracy: 0.58316 Testing AUC: 0.83287\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training_Statistics: \n",
      " Epoch: 36/10000 Training loss: 1.12718 Validation loss: 1.28148 Testing loss: 1.29186 Training Accuracy: 0.62786 Validation Accuracy: 0.56879 Testing AUC: 0.83361\n",
      "Training_Statistics: \n",
      " Epoch: 37/10000 Training loss: 1.08543 Validation loss: 1.27776 Testing loss: 1.28438 Training Accuracy: 0.6341 Validation Accuracy: 0.56468 Testing AUC: 0.83661\n",
      "Training_Statistics: \n",
      " Epoch: 38/10000 Training loss: 1.06171 Validation loss: 1.27973 Testing loss: 1.28247 Training Accuracy: 0.65137 Validation Accuracy: 0.57495 Testing AUC: 0.83845\n",
      "Training_Statistics: \n",
      " Epoch: 39/10000 Training loss: 1.04762 Validation loss: 1.27495 Testing loss: 1.27952 Training Accuracy: 0.65937 Validation Accuracy: 0.56879 Testing AUC: 0.83858\n",
      "Training_Statistics: \n",
      " Epoch: 40/10000 Training loss: 1.03837 Validation loss: 1.26853 Testing loss: 1.27424 Training Accuracy: 0.66353 Validation Accuracy: 0.56674 Testing AUC: 0.83936\n",
      "Training_Statistics: \n",
      " Epoch: 41/10000 Training loss: 1.02816 Validation loss: 1.26389 Testing loss: 1.26642 Training Accuracy: 0.66859 Validation Accuracy: 0.56468 Testing AUC: 0.84225\n",
      "Training_Statistics: \n",
      " Epoch: 42/10000 Training loss: 1.01265 Validation loss: 1.27106 Testing loss: 1.26857 Training Accuracy: 0.66921 Validation Accuracy: 0.577 Testing AUC: 0.84389\n",
      "Training_Statistics: \n",
      " Epoch: 43/10000 Training loss: 0.98940 Validation loss: 1.26356 Testing loss: 1.26336 Training Accuracy: 0.68753 Validation Accuracy: 0.577 Testing AUC: 0.84413\n",
      "Training_Statistics: \n",
      " Epoch: 44/10000 Training loss: 0.98790 Validation loss: 1.25700 Testing loss: 1.26085 Training Accuracy: 0.68839 Validation Accuracy: 0.56468 Testing AUC: 0.84457\n",
      "Training_Statistics: \n",
      " Epoch: 45/10000 Training loss: 0.97962 Validation loss: 1.26301 Testing loss: 1.26071 Training Accuracy: 0.68329 Validation Accuracy: 0.57084 Testing AUC: 0.84742\n",
      "Training_Statistics: \n",
      " Epoch: 46/10000 Training loss: 0.95921 Validation loss: 1.26783 Testing loss: 1.26102 Training Accuracy: 0.69607 Validation Accuracy: 0.577 Testing AUC: 0.84697\n",
      "Training_Statistics: \n",
      " Epoch: 47/10000 Training loss: 0.95254 Validation loss: 1.26817 Testing loss: 1.26107 Training Accuracy: 0.69301 Validation Accuracy: 0.56263 Testing AUC: 0.84645\n",
      "Training_Statistics: \n",
      " Epoch: 48/10000 Training loss: 0.90949 Validation loss: 1.25606 Testing loss: 1.24627 Training Accuracy: 0.71009 Validation Accuracy: 0.55441 Testing AUC: 0.85023\n",
      "Training_Statistics: \n",
      " Epoch: 49/10000 Training loss: 0.92125 Validation loss: 1.26255 Testing loss: 1.25124 Training Accuracy: 0.70615 Validation Accuracy: 0.58932 Testing AUC: 0.85136\n",
      "Training_Statistics: \n",
      " Epoch: 50/10000 Training loss: 0.90119 Validation loss: 1.25959 Testing loss: 1.24725 Training Accuracy: 0.71071 Validation Accuracy: 0.577 Testing AUC: 0.85214\n",
      "Training_Statistics: \n",
      " Epoch: 51/10000 Training loss: 0.87940 Validation loss: 1.26555 Testing loss: 1.24767 Training Accuracy: 0.70963 Validation Accuracy: 0.56674 Testing AUC: 0.85213\n",
      "Training_Statistics: \n",
      " Epoch: 52/10000 Training loss: 0.87459 Validation loss: 1.27160 Testing loss: 1.24208 Training Accuracy: 0.72715 Validation Accuracy: 0.58316 Testing AUC: 0.85337\n",
      "Training_Statistics: \n",
      " Epoch: 53/10000 Training loss: 0.86873 Validation loss: 1.27584 Testing loss: 1.24510 Training Accuracy: 0.72095 Validation Accuracy: 0.58316 Testing AUC: 0.85518\n",
      "Training_Statistics: \n",
      " Epoch: 54/10000 Training loss: 0.84617 Validation loss: 1.26837 Testing loss: 1.24761 Training Accuracy: 0.72801 Validation Accuracy: 0.57495 Testing AUC: 0.85383\n",
      "Done Training\n",
      "Accuracy = 0.567419575633128\n",
      "3\n",
      "Training_Statistics: \n",
      " Epoch: 1/10000 Training loss: 2.19667 Validation loss: 2.08591 Testing loss: 2.08820 Training Accuracy: 0.11795 Validation Accuracy: 0.31622 Testing AUC: 0.46478\n",
      "Training_Statistics: \n",
      " Epoch: 2/10000 Training loss: 2.06523 Validation loss: 1.95966 Testing loss: 1.97608 Training Accuracy: 0.31575 Validation Accuracy: 0.37577 Testing AUC: 0.48849\n",
      "Training_Statistics: \n",
      " Epoch: 3/10000 Training loss: 1.94000 Validation loss: 1.87220 Testing loss: 1.90504 Training Accuracy: 0.35714 Validation Accuracy: 0.37577 Testing AUC: 0.51513\n",
      "Training_Statistics: \n",
      " Epoch: 4/10000 Training loss: 1.89693 Validation loss: 1.84998 Testing loss: 1.89878 Training Accuracy: 0.34903 Validation Accuracy: 0.37577 Testing AUC: 0.54718\n",
      "Training_Statistics: \n",
      " Epoch: 5/10000 Training loss: 1.90350 Validation loss: 1.83138 Testing loss: 1.88282 Training Accuracy: 0.33743 Validation Accuracy: 0.37577 Testing AUC: 0.57734\n",
      "Training_Statistics: \n",
      " Epoch: 6/10000 Training loss: 1.84923 Validation loss: 1.81312 Testing loss: 1.85802 Training Accuracy: 0.34555 Validation Accuracy: 0.37577 Testing AUC: 0.60664\n",
      "Training_Statistics: \n",
      " Epoch: 7/10000 Training loss: 1.83642 Validation loss: 1.80817 Testing loss: 1.84280 Training Accuracy: 0.35019 Validation Accuracy: 0.38193 Testing AUC: 0.63423\n",
      "Training_Statistics: \n",
      " Epoch: 8/10000 Training loss: 1.82288 Validation loss: 1.79597 Testing loss: 1.82174 Training Accuracy: 0.35057 Validation Accuracy: 0.41068 Testing AUC: 0.66082\n",
      "Training_Statistics: \n",
      " Epoch: 9/10000 Training loss: 1.79735 Validation loss: 1.77661 Testing loss: 1.79616 Training Accuracy: 0.40002 Validation Accuracy: 0.423 Testing AUC: 0.68322\n",
      "Training_Statistics: \n",
      " Epoch: 10/10000 Training loss: 1.76332 Validation loss: 1.75241 Testing loss: 1.76920 Training Accuracy: 0.41342 Validation Accuracy: 0.423 Testing AUC: 0.70284\n",
      "Training_Statistics: \n",
      " Epoch: 11/10000 Training loss: 1.74412 Validation loss: 1.72801 Testing loss: 1.74498 Training Accuracy: 0.39598 Validation Accuracy: 0.41684 Testing AUC: 0.71981\n",
      "Training_Statistics: \n",
      " Epoch: 12/10000 Training loss: 1.73013 Validation loss: 1.70559 Testing loss: 1.72495 Training Accuracy: 0.38893 Validation Accuracy: 0.41889 Testing AUC: 0.73456\n",
      "Training_Statistics: \n",
      " Epoch: 13/10000 Training loss: 1.69181 Validation loss: 1.68394 Testing loss: 1.70646 Training Accuracy: 0.403 Validation Accuracy: 0.42094 Testing AUC: 0.7473\n",
      "Training_Statistics: \n",
      " Epoch: 14/10000 Training loss: 1.67447 Validation loss: 1.66123 Testing loss: 1.68675 Training Accuracy: 0.39994 Validation Accuracy: 0.43532 Testing AUC: 0.75811\n",
      "Training_Statistics: \n",
      " Epoch: 15/10000 Training loss: 1.63922 Validation loss: 1.63819 Testing loss: 1.66557 Training Accuracy: 0.41822 Validation Accuracy: 0.44353 Testing AUC: 0.76772\n",
      "Training_Statistics: \n",
      " Epoch: 16/10000 Training loss: 1.63868 Validation loss: 1.61615 Testing loss: 1.64387 Training Accuracy: 0.431 Validation Accuracy: 0.44764 Testing AUC: 0.77443\n",
      "Training_Statistics: \n",
      " Epoch: 17/10000 Training loss: 1.61875 Validation loss: 1.59470 Testing loss: 1.62097 Training Accuracy: 0.44874 Validation Accuracy: 0.4538 Testing AUC: 0.78031\n",
      "Training_Statistics: \n",
      " Epoch: 18/10000 Training loss: 1.58443 Validation loss: 1.57272 Testing loss: 1.59674 Training Accuracy: 0.46336 Validation Accuracy: 0.44764 Testing AUC: 0.78475\n",
      "Training_Statistics: \n",
      " Epoch: 19/10000 Training loss: 1.56906 Validation loss: 1.54925 Testing loss: 1.57018 Training Accuracy: 0.4613 Validation Accuracy: 0.4538 Testing AUC: 0.78789\n",
      "Training_Statistics: \n",
      " Epoch: 20/10000 Training loss: 1.50608 Validation loss: 1.52540 Testing loss: 1.54256 Training Accuracy: 0.48502 Validation Accuracy: 0.4538 Testing AUC: 0.79164\n",
      "Training_Statistics: \n",
      " Epoch: 21/10000 Training loss: 1.48111 Validation loss: 1.50259 Testing loss: 1.51626 Training Accuracy: 0.48812 Validation Accuracy: 0.46407 Testing AUC: 0.79523\n",
      "Training_Statistics: \n",
      " Epoch: 22/10000 Training loss: 1.44990 Validation loss: 1.48124 Testing loss: 1.49158 Training Accuracy: 0.48532 Validation Accuracy: 0.47023 Testing AUC: 0.79874\n",
      "Training_Statistics: \n",
      " Epoch: 23/10000 Training loss: 1.44022 Validation loss: 1.46084 Testing loss: 1.46797 Training Accuracy: 0.49666 Validation Accuracy: 0.48255 Testing AUC: 0.80237\n",
      "Training_Statistics: \n",
      " Epoch: 24/10000 Training loss: 1.38845 Validation loss: 1.44186 Testing loss: 1.44693 Training Accuracy: 0.53004 Validation Accuracy: 0.48049 Testing AUC: 0.80539\n",
      "Training_Statistics: \n",
      " Epoch: 25/10000 Training loss: 1.36673 Validation loss: 1.42178 Testing loss: 1.42376 Training Accuracy: 0.52858 Validation Accuracy: 0.48049 Testing AUC: 0.80808\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training_Statistics: \n",
      " Epoch: 26/10000 Training loss: 1.35026 Validation loss: 1.40073 Testing loss: 1.40014 Training Accuracy: 0.54014 Validation Accuracy: 0.48871 Testing AUC: 0.81001\n",
      "Training_Statistics: \n",
      " Epoch: 27/10000 Training loss: 1.32383 Validation loss: 1.38425 Testing loss: 1.38188 Training Accuracy: 0.54356 Validation Accuracy: 0.48255 Testing AUC: 0.81127\n",
      "Training_Statistics: \n",
      " Epoch: 28/10000 Training loss: 1.29567 Validation loss: 1.36892 Testing loss: 1.36797 Training Accuracy: 0.54042 Validation Accuracy: 0.49487 Testing AUC: 0.81351\n",
      "Training_Statistics: \n",
      " Epoch: 29/10000 Training loss: 1.27354 Validation loss: 1.35907 Testing loss: 1.36172 Training Accuracy: 0.55664 Validation Accuracy: 0.50308 Testing AUC: 0.81567\n",
      "Training_Statistics: \n",
      " Epoch: 30/10000 Training loss: 1.25292 Validation loss: 1.35208 Testing loss: 1.35494 Training Accuracy: 0.572 Validation Accuracy: 0.50924 Testing AUC: 0.81804\n",
      "Training_Statistics: \n",
      " Epoch: 31/10000 Training loss: 1.22439 Validation loss: 1.33952 Testing loss: 1.34057 Training Accuracy: 0.57906 Validation Accuracy: 0.50719 Testing AUC: 0.82027\n",
      "Training_Statistics: \n",
      " Epoch: 32/10000 Training loss: 1.19803 Validation loss: 1.32963 Testing loss: 1.32183 Training Accuracy: 0.57494 Validation Accuracy: 0.51745 Testing AUC: 0.8226\n",
      "Training_Statistics: \n",
      " Epoch: 33/10000 Training loss: 1.19182 Validation loss: 1.32020 Testing loss: 1.31209 Training Accuracy: 0.59356 Validation Accuracy: 0.52567 Testing AUC: 0.82465\n",
      "Training_Statistics: \n",
      " Epoch: 34/10000 Training loss: 1.17160 Validation loss: 1.31124 Testing loss: 1.30868 Training Accuracy: 0.61317 Validation Accuracy: 0.51745 Testing AUC: 0.82673\n",
      "Training_Statistics: \n",
      " Epoch: 35/10000 Training loss: 1.16384 Validation loss: 1.30387 Testing loss: 1.30822 Training Accuracy: 0.61704 Validation Accuracy: 0.51745 Testing AUC: 0.82785\n",
      "Training_Statistics: \n",
      " Epoch: 36/10000 Training loss: 1.13591 Validation loss: 1.29666 Testing loss: 1.30191 Training Accuracy: 0.62883 Validation Accuracy: 0.52567 Testing AUC: 0.82924\n",
      "Training_Statistics: \n",
      " Epoch: 37/10000 Training loss: 1.10171 Validation loss: 1.29443 Testing loss: 1.29178 Training Accuracy: 0.63781 Validation Accuracy: 0.53388 Testing AUC: 0.83057\n",
      "Training_Statistics: \n",
      " Epoch: 38/10000 Training loss: 1.09673 Validation loss: 1.29175 Testing loss: 1.28637 Training Accuracy: 0.64505 Validation Accuracy: 0.53799 Testing AUC: 0.83133\n",
      "Training_Statistics: \n",
      " Epoch: 39/10000 Training loss: 1.08115 Validation loss: 1.29026 Testing loss: 1.28817 Training Accuracy: 0.64689 Validation Accuracy: 0.52156 Testing AUC: 0.83227\n",
      "Training_Statistics: \n",
      " Epoch: 40/10000 Training loss: 1.06159 Validation loss: 1.28821 Testing loss: 1.28579 Training Accuracy: 0.65263 Validation Accuracy: 0.51745 Testing AUC: 0.83268\n",
      "Training_Statistics: \n",
      " Epoch: 41/10000 Training loss: 1.04636 Validation loss: 1.28591 Testing loss: 1.28086 Training Accuracy: 0.65929 Validation Accuracy: 0.54415 Testing AUC: 0.83312\n",
      "Training_Statistics: \n",
      " Epoch: 42/10000 Training loss: 1.03258 Validation loss: 1.28681 Testing loss: 1.27927 Training Accuracy: 0.66103 Validation Accuracy: 0.53593 Testing AUC: 0.83337\n",
      "Training_Statistics: \n",
      " Epoch: 43/10000 Training loss: 1.03308 Validation loss: 1.29179 Testing loss: 1.28716 Training Accuracy: 0.66215 Validation Accuracy: 0.51951 Testing AUC: 0.8332\n",
      "Training_Statistics: \n",
      " Epoch: 44/10000 Training loss: 1.01224 Validation loss: 1.29006 Testing loss: 1.28349 Training Accuracy: 0.66463 Validation Accuracy: 0.53593 Testing AUC: 0.83368\n",
      "Training_Statistics: \n",
      " Epoch: 45/10000 Training loss: 0.99041 Validation loss: 1.29060 Testing loss: 1.27359 Training Accuracy: 0.67559 Validation Accuracy: 0.54209 Testing AUC: 0.834\n",
      "Training_Statistics: \n",
      " Epoch: 46/10000 Training loss: 0.96194 Validation loss: 1.28770 Testing loss: 1.27338 Training Accuracy: 0.70019 Validation Accuracy: 0.53799 Testing AUC: 0.83427\n",
      "Training_Statistics: \n",
      " Epoch: 47/10000 Training loss: 0.95756 Validation loss: 1.28419 Testing loss: 1.28234 Training Accuracy: 0.69141 Validation Accuracy: 0.54209 Testing AUC: 0.83417\n",
      "Training_Statistics: \n",
      " Epoch: 48/10000 Training loss: 0.94822 Validation loss: 1.28832 Testing loss: 1.28165 Training Accuracy: 0.69373 Validation Accuracy: 0.53799 Testing AUC: 0.83399\n",
      "Training_Statistics: \n",
      " Epoch: 49/10000 Training loss: 0.94030 Validation loss: 1.29298 Testing loss: 1.27843 Training Accuracy: 0.69731 Validation Accuracy: 0.53799 Testing AUC: 0.83491\n",
      "Training_Statistics: \n",
      " Epoch: 50/10000 Training loss: 0.91787 Validation loss: 1.29128 Testing loss: 1.28114 Training Accuracy: 0.70771 Validation Accuracy: 0.5462 Testing AUC: 0.83544\n",
      "Training_Statistics: \n",
      " Epoch: 51/10000 Training loss: 0.89769 Validation loss: 1.28969 Testing loss: 1.29446 Training Accuracy: 0.70797 Validation Accuracy: 0.55236 Testing AUC: 0.83561\n",
      "Done Training\n",
      "Accuracy = 0.5621149897330595\n",
      "4\n",
      "Training_Statistics: \n",
      " Epoch: 1/10000 Training loss: 2.11988 Validation loss: 2.01043 Testing loss: 2.01202 Training Accuracy: 0.25818 Validation Accuracy: 0.35934 Testing AUC: 0.51737\n",
      "Training_Statistics: \n",
      " Epoch: 2/10000 Training loss: 1.98430 Validation loss: 1.89572 Testing loss: 1.89716 Training Accuracy: 0.34854 Validation Accuracy: 0.35729 Testing AUC: 0.54152\n",
      "Training_Statistics: \n",
      " Epoch: 3/10000 Training loss: 1.89287 Validation loss: 1.85022 Testing loss: 1.85255 Training Accuracy: 0.35705 Validation Accuracy: 0.35729 Testing AUC: 0.56584\n",
      "Training_Statistics: \n",
      " Epoch: 4/10000 Training loss: 1.85781 Validation loss: 1.83067 Testing loss: 1.83259 Training Accuracy: 0.35647 Validation Accuracy: 0.35729 Testing AUC: 0.59686\n",
      "Training_Statistics: \n",
      " Epoch: 5/10000 Training loss: 1.84692 Validation loss: 1.80170 Testing loss: 1.80254 Training Accuracy: 0.35413 Validation Accuracy: 0.35729 Testing AUC: 0.62916\n",
      "Training_Statistics: \n",
      " Epoch: 6/10000 Training loss: 1.84108 Validation loss: 1.78644 Testing loss: 1.78460 Training Accuracy: 0.3401 Validation Accuracy: 0.37166 Testing AUC: 0.65781\n",
      "Training_Statistics: \n",
      " Epoch: 7/10000 Training loss: 1.79753 Validation loss: 1.77965 Testing loss: 1.77309 Training Accuracy: 0.38132 Validation Accuracy: 0.40862 Testing AUC: 0.68277\n",
      "Training_Statistics: \n",
      " Epoch: 8/10000 Training loss: 1.79259 Validation loss: 1.77073 Testing loss: 1.75895 Training Accuracy: 0.39672 Validation Accuracy: 0.40862 Testing AUC: 0.70404\n",
      "Training_Statistics: \n",
      " Epoch: 9/10000 Training loss: 1.75821 Validation loss: 1.75649 Testing loss: 1.74021 Training Accuracy: 0.40799 Validation Accuracy: 0.40657 Testing AUC: 0.72307\n",
      "Training_Statistics: \n",
      " Epoch: 10/10000 Training loss: 1.73955 Validation loss: 1.73791 Testing loss: 1.71846 Training Accuracy: 0.40674 Validation Accuracy: 0.40657 Testing AUC: 0.73808\n",
      "Training_Statistics: \n",
      " Epoch: 11/10000 Training loss: 1.71084 Validation loss: 1.71670 Testing loss: 1.69536 Training Accuracy: 0.40374 Validation Accuracy: 0.40657 Testing AUC: 0.75131\n",
      "Training_Statistics: \n",
      " Epoch: 12/10000 Training loss: 1.70528 Validation loss: 1.69288 Testing loss: 1.67010 Training Accuracy: 0.4044 Validation Accuracy: 0.41068 Testing AUC: 0.76319\n",
      "Training_Statistics: \n",
      " Epoch: 13/10000 Training loss: 1.67123 Validation loss: 1.66729 Testing loss: 1.64336 Training Accuracy: 0.42134 Validation Accuracy: 0.41684 Testing AUC: 0.7758\n",
      "Training_Statistics: \n",
      " Epoch: 14/10000 Training loss: 1.63984 Validation loss: 1.64373 Testing loss: 1.61785 Training Accuracy: 0.43251 Validation Accuracy: 0.42916 Testing AUC: 0.78514\n",
      "Training_Statistics: \n",
      " Epoch: 15/10000 Training loss: 1.62445 Validation loss: 1.62260 Testing loss: 1.59403 Training Accuracy: 0.43183 Validation Accuracy: 0.43943 Testing AUC: 0.79346\n",
      "Training_Statistics: \n",
      " Epoch: 16/10000 Training loss: 1.58564 Validation loss: 1.60147 Testing loss: 1.57015 Training Accuracy: 0.45427 Validation Accuracy: 0.4538 Testing AUC: 0.80135\n",
      "Training_Statistics: \n",
      " Epoch: 17/10000 Training loss: 1.55145 Validation loss: 1.58007 Testing loss: 1.54540 Training Accuracy: 0.47653 Validation Accuracy: 0.45996 Testing AUC: 0.80748\n",
      "Training_Statistics: \n",
      " Epoch: 18/10000 Training loss: 1.52717 Validation loss: 1.55920 Testing loss: 1.51989 Training Accuracy: 0.48611 Validation Accuracy: 0.46817 Testing AUC: 0.81314\n",
      "Training_Statistics: \n",
      " Epoch: 19/10000 Training loss: 1.49181 Validation loss: 1.53850 Testing loss: 1.49330 Training Accuracy: 0.4978 Validation Accuracy: 0.47228 Testing AUC: 0.81809\n",
      "Training_Statistics: \n",
      " Epoch: 20/10000 Training loss: 1.47145 Validation loss: 1.51750 Testing loss: 1.46572 Training Accuracy: 0.49437 Validation Accuracy: 0.48665 Testing AUC: 0.82221\n",
      "Training_Statistics: \n",
      " Epoch: 21/10000 Training loss: 1.44090 Validation loss: 1.49859 Testing loss: 1.43924 Training Accuracy: 0.50563 Validation Accuracy: 0.48665 Testing AUC: 0.82562\n",
      "Training_Statistics: \n",
      " Epoch: 22/10000 Training loss: 1.42763 Validation loss: 1.48259 Testing loss: 1.41594 Training Accuracy: 0.51463 Validation Accuracy: 0.47433 Testing AUC: 0.82813\n",
      "Training_Statistics: \n",
      " Epoch: 23/10000 Training loss: 1.39117 Validation loss: 1.46907 Testing loss: 1.39716 Training Accuracy: 0.51653 Validation Accuracy: 0.48871 Testing AUC: 0.83065\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training_Statistics: \n",
      " Epoch: 24/10000 Training loss: 1.34514 Validation loss: 1.45771 Testing loss: 1.38241 Training Accuracy: 0.53423 Validation Accuracy: 0.48255 Testing AUC: 0.8317\n",
      "Training_Statistics: \n",
      " Epoch: 25/10000 Training loss: 1.34173 Validation loss: 1.44403 Testing loss: 1.36754 Training Accuracy: 0.5263 Validation Accuracy: 0.49076 Testing AUC: 0.83266\n",
      "Training_Statistics: \n",
      " Epoch: 26/10000 Training loss: 1.29992 Validation loss: 1.43743 Testing loss: 1.35550 Training Accuracy: 0.54775 Validation Accuracy: 0.48665 Testing AUC: 0.83381\n",
      "Training_Statistics: \n",
      " Epoch: 27/10000 Training loss: 1.27871 Validation loss: 1.43357 Testing loss: 1.34732 Training Accuracy: 0.54882 Validation Accuracy: 0.48665 Testing AUC: 0.83498\n",
      "Training_Statistics: \n",
      " Epoch: 28/10000 Training loss: 1.24969 Validation loss: 1.42393 Testing loss: 1.33728 Training Accuracy: 0.56192 Validation Accuracy: 0.49281 Testing AUC: 0.8364\n",
      "Training_Statistics: \n",
      " Epoch: 29/10000 Training loss: 1.26411 Validation loss: 1.40537 Testing loss: 1.32410 Training Accuracy: 0.56349 Validation Accuracy: 0.49076 Testing AUC: 0.83738\n",
      "Training_Statistics: \n",
      " Epoch: 30/10000 Training loss: 1.23576 Validation loss: 1.39445 Testing loss: 1.31470 Training Accuracy: 0.58617 Validation Accuracy: 0.50103 Testing AUC: 0.83766\n",
      "Training_Statistics: \n",
      " Epoch: 31/10000 Training loss: 1.21882 Validation loss: 1.39407 Testing loss: 1.31165 Training Accuracy: 0.59067 Validation Accuracy: 0.50719 Testing AUC: 0.83787\n",
      "Training_Statistics: \n",
      " Epoch: 32/10000 Training loss: 1.18462 Validation loss: 1.39106 Testing loss: 1.31121 Training Accuracy: 0.60527 Validation Accuracy: 0.52361 Testing AUC: 0.83841\n",
      "Training_Statistics: \n",
      " Epoch: 33/10000 Training loss: 1.15868 Validation loss: 1.38022 Testing loss: 1.30529 Training Accuracy: 0.60953 Validation Accuracy: 0.52361 Testing AUC: 0.83936\n",
      "Training_Statistics: \n",
      " Epoch: 34/10000 Training loss: 1.13558 Validation loss: 1.36822 Testing loss: 1.29485 Training Accuracy: 0.63197 Validation Accuracy: 0.52156 Testing AUC: 0.84001\n",
      "Training_Statistics: \n",
      " Epoch: 35/10000 Training loss: 1.14688 Validation loss: 1.37109 Testing loss: 1.29239 Training Accuracy: 0.61301 Validation Accuracy: 0.52361 Testing AUC: 0.84082\n",
      "Training_Statistics: \n",
      " Epoch: 36/10000 Training loss: 1.11105 Validation loss: 1.38010 Testing loss: 1.29949 Training Accuracy: 0.63387 Validation Accuracy: 0.51335 Testing AUC: 0.84207\n",
      "Training_Statistics: \n",
      " Epoch: 37/10000 Training loss: 1.10196 Validation loss: 1.36926 Testing loss: 1.29518 Training Accuracy: 0.64371 Validation Accuracy: 0.52156 Testing AUC: 0.84326\n",
      "Training_Statistics: \n",
      " Epoch: 38/10000 Training loss: 1.10050 Validation loss: 1.35712 Testing loss: 1.28981 Training Accuracy: 0.6362 Validation Accuracy: 0.51745 Testing AUC: 0.84376\n",
      "Training_Statistics: \n",
      " Epoch: 39/10000 Training loss: 1.07828 Validation loss: 1.36146 Testing loss: 1.28510 Training Accuracy: 0.64646 Validation Accuracy: 0.5154 Testing AUC: 0.8443\n",
      "Training_Statistics: \n",
      " Epoch: 40/10000 Training loss: 1.06512 Validation loss: 1.37564 Testing loss: 1.29116 Training Accuracy: 0.65463 Validation Accuracy: 0.53183 Testing AUC: 0.84548\n",
      "Training_Statistics: \n",
      " Epoch: 41/10000 Training loss: 1.02640 Validation loss: 1.37112 Testing loss: 1.28916 Training Accuracy: 0.66615 Validation Accuracy: 0.52977 Testing AUC: 0.84675\n",
      "Training_Statistics: \n",
      " Epoch: 42/10000 Training loss: 1.03184 Validation loss: 1.36225 Testing loss: 1.28395 Training Accuracy: 0.66815 Validation Accuracy: 0.51745 Testing AUC: 0.84815\n",
      "Training_Statistics: \n",
      " Epoch: 43/10000 Training loss: 1.01638 Validation loss: 1.36632 Testing loss: 1.28359 Training Accuracy: 0.66105 Validation Accuracy: 0.51335 Testing AUC: 0.84864\n",
      "Training_Statistics: \n",
      " Epoch: 44/10000 Training loss: 0.99032 Validation loss: 1.37653 Testing loss: 1.28432 Training Accuracy: 0.67807 Validation Accuracy: 0.52772 Testing AUC: 0.8498\n",
      "Training_Statistics: \n",
      " Epoch: 45/10000 Training loss: 0.99447 Validation loss: 1.36235 Testing loss: 1.27786 Training Accuracy: 0.67539 Validation Accuracy: 0.53388 Testing AUC: 0.84985\n",
      "Training_Statistics: \n",
      " Epoch: 46/10000 Training loss: 0.98187 Validation loss: 1.35662 Testing loss: 1.27687 Training Accuracy: 0.67589 Validation Accuracy: 0.52977 Testing AUC: 0.8494\n",
      "Training_Statistics: \n",
      " Epoch: 47/10000 Training loss: 0.97237 Validation loss: 1.36562 Testing loss: 1.28336 Training Accuracy: 0.68582 Validation Accuracy: 0.53799 Testing AUC: 0.84957\n",
      "Training_Statistics: \n",
      " Epoch: 48/10000 Training loss: 0.95938 Validation loss: 1.37351 Testing loss: 1.28655 Training Accuracy: 0.68181 Validation Accuracy: 0.53183 Testing AUC: 0.85095\n",
      "Training_Statistics: \n",
      " Epoch: 49/10000 Training loss: 0.95574 Validation loss: 1.36394 Testing loss: 1.27400 Training Accuracy: 0.68706 Validation Accuracy: 0.53183 Testing AUC: 0.85067\n",
      "Training_Statistics: \n",
      " Epoch: 50/10000 Training loss: 0.91937 Validation loss: 1.36310 Testing loss: 1.27182 Training Accuracy: 0.69991 Validation Accuracy: 0.52977 Testing AUC: 0.8506\n",
      "Training_Statistics: \n",
      " Epoch: 51/10000 Training loss: 0.91562 Validation loss: 1.36316 Testing loss: 1.27662 Training Accuracy: 0.69456 Validation Accuracy: 0.54004 Testing AUC: 0.85168\n",
      "Training_Statistics: \n",
      " Epoch: 52/10000 Training loss: 0.88647 Validation loss: 1.35947 Testing loss: 1.27406 Training Accuracy: 0.71418 Validation Accuracy: 0.53799 Testing AUC: 0.85179\n",
      "Training_Statistics: \n",
      " Epoch: 53/10000 Training loss: 0.88533 Validation loss: 1.36568 Testing loss: 1.27574 Training Accuracy: 0.71517 Validation Accuracy: 0.54004 Testing AUC: 0.85226\n",
      "Training_Statistics: \n",
      " Epoch: 54/10000 Training loss: 0.87486 Validation loss: 1.36617 Testing loss: 1.26929 Training Accuracy: 0.71416 Validation Accuracy: 0.52361 Testing AUC: 0.85258\n",
      "Training_Statistics: \n",
      " Epoch: 55/10000 Training loss: 0.85745 Validation loss: 1.36187 Testing loss: 1.26716 Training Accuracy: 0.72293 Validation Accuracy: 0.53388 Testing AUC: 0.85255\n",
      "Training_Statistics: \n",
      " Epoch: 56/10000 Training loss: 0.84877 Validation loss: 1.37190 Testing loss: 1.27976 Training Accuracy: 0.72926 Validation Accuracy: 0.54415 Testing AUC: 0.85303\n",
      "Done Training\n",
      "Accuracy = 0.5621665982765696\n",
      "K-fold Cross Validation Completed\n"
     ]
    }
   ],
   "source": [
    "DTCR_SS.K_Fold_CrossVal(folds=5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Once our algorithm has been trained, we may want to see which sequences are the most strongly predicted for each label. To do this we will run the following command. The output of the command is a dictionary of dataframes within the object we can view. Additionally, these dataframes can be found in the results folder underneath the subdirectory 'Rep_Sequences'."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "DTCR_SS.Representative_Sequences()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    alpha           beta      v_beta d_beta      j_beta v_alpha j_alpha  \\\n",
      "28   None  CASSPGQNTEVFF  TCRBV19-03   None  TCRBJ01-01    None    None   \n",
      "31   None  CASSPGTNTEVFF  TCRBV19-03   None  TCRBJ01-01    None    None   \n",
      "14   None  CASSPGTDTEVFF  TCRBV19-03   None  TCRBJ01-01    None    None   \n",
      "1    None  CASSIGDYAEQFF  TCRBV19-01   None  TCRBJ02-01    None    None   \n",
      "34   None  CASSWGANTEVFF  TCRBV19-03   None  TCRBJ01-01    None    None   \n",
      "27   None  CASSPGANSDYTF  TCRBV19-03   None  TCRBJ01-02    None    None   \n",
      "25   None  CASSPGASSDYTF  TCRBV19-03   None  TCRBJ01-02    None    None   \n",
      "100  None  CASSIGDNYEQYF  TCRBV19-01   None  TCRBJ02-07    None    None   \n",
      "37   None  CASSTGTGMEQYF  TCRBV19-01   None  TCRBJ02-07    None    None   \n",
      "0    None  CASSPGDYAEQFF  TCRBV19-03   None  TCRBJ02-01    None    None   \n",
      "\n",
      "     Class     Sample      Freq  ...           HLA     Db-F2    Db-M45  \\\n",
      "28   Db-F2  Db-F2.tsv  0.008547  ...  [N, o, n, e]  0.848162  0.005202   \n",
      "31   Db-F2  Db-F2.tsv  0.008547  ...  [N, o, n, e]  0.815924  0.012474   \n",
      "14   Db-F2  Db-F2.tsv  0.008547  ...  [N, o, n, e]  0.806967  0.028248   \n",
      "1    Db-F2  Db-F2.tsv  0.025641  ...  [N, o, n, e]  0.776084  0.007128   \n",
      "34   Db-F2  Db-F2.tsv  0.008547  ...  [N, o, n, e]  0.738826  0.010588   \n",
      "27   Db-F2  Db-F2.tsv  0.008547  ...  [N, o, n, e]  0.725819  0.131070   \n",
      "25   Db-F2  Db-F2.tsv  0.008547  ...  [N, o, n, e]  0.713926  0.128577   \n",
      "100  Db-F2  Db-F2.tsv  0.008547  ...  [N, o, n, e]  0.691658  0.040259   \n",
      "37   Db-F2  Db-F2.tsv  0.008547  ...  [N, o, n, e]  0.674476  0.021840   \n",
      "0    Db-F2  Db-F2.tsv  0.025641  ...  [N, o, n, e]  0.671330  0.065498   \n",
      "\n",
      "        Db-NP     Db-PA    Db-PB1    Kb-M38    Kb-SIY   Kb-TRP2   Kb-m139  \n",
      "28   0.045135  0.002000  0.003793  0.002366  0.058229  0.021473  0.013639  \n",
      "31   0.026284  0.009885  0.024441  0.002393  0.059979  0.013625  0.034995  \n",
      "14   0.023913  0.009196  0.049237  0.006077  0.030990  0.017210  0.028161  \n",
      "1    0.017973  0.004176  0.041415  0.009291  0.098982  0.017555  0.027396  \n",
      "34   0.110140  0.018834  0.011695  0.002006  0.052311  0.024655  0.030945  \n",
      "27   0.015229  0.022059  0.003293  0.002143  0.071290  0.022891  0.006206  \n",
      "25   0.060312  0.023802  0.022290  0.008699  0.014047  0.022621  0.005725  \n",
      "100  0.041922  0.020031  0.028768  0.018701  0.110170  0.036518  0.011972  \n",
      "37   0.045446  0.008161  0.028311  0.022239  0.137028  0.020640  0.041859  \n",
      "0    0.021907  0.031956  0.032246  0.018611  0.114375  0.018283  0.025794  \n",
      "\n",
      "[10 rows x 21 columns]\n"
     ]
    }
   ],
   "source": [
    "print(DTCR_SS.Rep_Seq['Db-F2'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Furthermore, we may want to know which learned motifs are associated with a given label. To do this, we can run the following command with the label we want to know the predictive motifs for."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Motif Identification Completed\n"
     ]
    }
   ],
   "source": [
    "DTCR_SS.Motif_Identification('Db-F2')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The motifs can then be found in fasta files in the results folder underneath (label)_(alpha/beta)_Motifs. These fasta fiels can then be used with \"https://weblogo.berkeley.edu/logo.cgi\" for motif visualization.The file names have the magnitude of the enrichment as the first number followed by '_feature_n'. The higher the number, the more enriched the motif is in the label-specific sequences."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Visualization"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can also visualize the learned latent space from the supervised sequence classifier through plotting a UMAP representation of the sequences in two dimensions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "DTCR_SS.UMAP_Plot(by_class=True,freq_weight=True,scale=1000)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can also specify whether we only want to plot sequences that were used in either train,valid, or the test set with the 'set' parameter."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "DTCR_SS.UMAP_Plot(by_class=True,freq_weight=True,scale=2000,set='test')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can also visualize how the repertoires are related from this learned representation. This visualiztion is helpful when we want to compare how different TCR repertoires are related structurally."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "UMAP transformation...\n",
      "PhenoGraph Clustering...\n",
      "Finding 30 nearest neighbors using minkowski metric and 'auto' algorithm\n",
      "Neighbors computed in 3.4246816635131836 seconds\n",
      "Jaccard graph constructed in 1.0992822647094727 seconds\n",
      "Wrote graph to binary file in 0.4135909080505371 seconds\n",
      "Running Louvain modularity optimization\n",
      "After 1 runs, maximum modularity is Q = 0.886302\n",
      "Louvain completed 21 runs in 4.5533435344696045 seconds\n",
      "PhenoGraph complete in 9.507049560546875 seconds\n",
      "Clustering Done\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 15 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "DTCR_SS.Repertoire_Dendrogram(gridsize=50,gaussian_sigma=0.75,lw=6,dendrogram_radius=0.3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "See documentation for how to use the full functionality of this method."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.8"
  }
 },
 "nbformat": 4,
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